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AI Product Engineering

Build AI Products That Ship and
Scale in Production

From architecture design to live deployment, Ment Tech Labs engineers AI-native products that perform at enterprise scale. We own the full stack: LLM integration, data pipelines, inference cost optimisation, MLOps monitoring, and EU AI Act compliance. Your team ships a product, not a demo.
  • 300+ AI Products Shipped
  • <72h to First Working Demo
  • 99.9% Production Uptime SLA
  • ISO/IEC 42001 Certified
Start Your AI Product
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AI Products Shipped to Production
0 +
Hours to First Working Demo
h
Uptime SLA Across Deployments
0 %
Average Weeks to Production
0 wks

Trusted & Certified

  • ISO 27001 Certified
  • SOC 2 Type II
  • 150+ AI Engineers
  • 80+ Projects Delivered
Quick Answer

What is AI Product Engineering?

AI product engineering is the discipline of designing, building, and operating AI-powered software products from concept to production scale. It covers LLM integration, ML model development and fine-tuning, inference infrastructure and cost optimisation, data pipelines, MLOps automation, security hardening, and user-facing application layers. These are delivered as a complete, maintainable system. Unlike AI consulting, which produces strategy documents, or standalone model development, which produces a model artefact, AI product engineering produces a shippable product: a web application, mobile app, API, or embedded system used daily by customers and internal teams, with monitoring and continuous improvement built in from launch.
Key Benefits

Full-stack ownership from data pipeline to user interface. No integration gaps. No handoff failures.
Production-ready architecture from week one: auto-scaling inference, cost optimisation, and MLOps included.
AI products built to enterprise standards: SOC 2, GDPR, EU AI Act compliance from day one.
60 to 80 percent inference cost reduction vs. naive LLM integration through intelligent routing and caching.

ISO 27001 · Certified

SOC 2 Type II · Compliant

Deloitte Fast 50 · Awarded

ERC-3643 · Compatible

KYC / AML · Integrated

MiCA-Ready · EU Compliant

VARA · UAE Licensed

OpenAI Partner · Certified

ISO 27001 · Certified

SOC 2 Type II · Compliant

Deloitte Fast 50 · Awarded

ERC-3643 · Compatible

KYC / AML · Integrated

MiCA-Ready · EU Compliant

VARA · UAE Licensed

OpenAI Partner · Certified

Industry Challenges

Why 87% of AI Products Never Ship to Production

The gap between a successful AI demo and a reliable, scalable AI product is not a model quality problem. It is an engineering, architecture, and operations problem.

Demo-to-Production Failure

An AI prototype that works in a Jupyter notebook collapses under production load. Latency spikes from 200ms to 8 seconds. Memory errors appear at scale. Integration failures cost 3 to 6 months and $500K+ to fix.

Siloed AI and Engineering Teams

Data scientists build models. Engineers build apps. Neither owns the full stack. The result is integration failures, ambiguous SLAs, unresolved latency issues, and ownership gaps that stall production launches for months.

Uncontrolled Inference Costs

Naive LLM integration routes every query through GPT-4 at full token cost. This creates $500K to $2M per year in API bills that make unit economics unviable before the first customer pricing discussion.

Silent Model Degradation

AI models degrade as real-world data distribution shifts away from training data. Without drift monitoring and automated retraining pipelines, product quality deteriorates silently while user complaints accumulate.

Security and Compliance as Afterthoughts

Prompt injection vulnerabilities, PII leakage in RAG pipelines, and EU AI Act non-compliance retrofitted after build cost 5x more than designing them in from the start.

Paralytic Iteration Cycles

Without MLOps pipelines and prompt versioning, updating a model or adjusting a prompt takes 2 to 4 weeks of manual coordination. This kills the rapid iteration velocity needed to improve AI product quality post-launch.

87%

AI Projects Never Reach Production (VentureBeat)

5x

Higher Cost to Retrofit Security vs. Build-In

40%

AI Production Failures Caused by Infra/Integration

8 wks

Ment Tech Labs Average Time to Production

The Cost of Inaction

Every month without a production-ready AI product is market share surrendered. Competitors with live AI products are compounding data moats and user switching costs that late movers cannot replicate regardless of model quality.

Our Solution

The Product Foundry Framework: From Brief to Production in 8 Weeks

Ment Tech Labs treats AI product engineering as a unified discipline. We own the full stack from week one, apply production-first architecture decisions from day one, and deliver a running product in 8 to 16 weeks.

Full-Stack AI Ownership

We design and build every layer: data pipelines, model serving, APIs, front-end application, and MLOps. No integration gaps. No ambiguous ownership. No finger-pointing between specialist teams during production incidents.

Production-First Architecture

Every design decision optimises for production performance, not demo quality. Auto-scaling, semantic caching, latency budgets, and cost controls are designed in from week one.

AI Cost Engineering

Intelligent model routing directs simple queries to cheaper models and complex queries to more capable ones. This reduces inference costs 60 to 80 percent without quality loss. Unit economics that hold at 10K users and at 10M users.

Continuous Improvement Loops

Automated evaluation pipelines, prompt versioning, A/B testing, and semantic drift detection. Your AI product improves continuously with every interaction, measured against domain-specific quality benchmarks.

Comparison

Traditional Software Development vs. AI-Native Product Engineering

Aspect
Legacy Method
RECOMMENDED AI-Native Method
Architecture Complexity
Deterministic logic, standard CRUD patterns
Probabilistic AI systems requiring evaluation harnesses, drift monitoring, and MLOps pipelines
Testing Approach
Unit tests and integration tests with pass/fail determinism
LLM evaluation harnesses, red-team adversarial testing, regression on AI output quality metrics
Deployment Cadence
Deploy once, patch on bug reports
Continuous model updates, prompt versioning, A/B testing, canary deployment for model changes
Performance Metrics
Latency, uptime, error rate
Model accuracy, hallucination rate, token cost per query, user CSAT, semantic drift metrics
Cost Model
Fixed infrastructure cost
Variable inference cost per query requiring intelligent routing, caching, and token optimisation
Data Requirements
Structured relational records
Training datasets, evaluation sets, vector embeddings, feature stores, RAG knowledge bases
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Core Capabilities

AI Product Engineering Capabilities

LLM Application Architecture

Production-grade LLM system design covering prompt management, context window optimisation, multi-turn conversation state, streaming responses, fallback model routing, and rate-limit handling. Built to sustain 99.9% availability under enterprise traffic.

  • Tools: LangChain, LangGraph, LlamaIndex orchestration. Multi-provider routing across GPT-4o, Claude 3.5, and Llama 3.1. Streaming via Server-Sent Events. Structured output parsing via JSON mode and Pydantic. Context window budget manager. Prompt versioning with PromptLayer.
  • Case example: Enterprise knowledge management platform supporting 12,000 daily active users at under 800ms P95 latency and 99.95% uptime over 6 months.

RAG Pipeline Engineering

Multi-stage retrieval systems with hybrid dense-sparse search, cross-encoder re-ranking, metadata filtering, and context compression. Reduces hallucinations 95%+ on enterprise knowledge bases. Keeps retrieval latency under 100ms P95.

  • Tools: Pinecone, Weaviate, Qdrant, pgvector vector stores. BM25 plus semantic hybrid search with reciprocal rank fusion. Cohere re-rank cross-encoder. Context compression via LLMLingua at 40% token reduction. RAGAS evaluation covering faithfulness, context precision, and answer relevancy.
  • Case example: Legal research platform indexing 10M+ case documents with 99.1% citation accuracy and under 95ms retrieval P95 across a multi-jurisdiction corpus.

AI Agent and Agentic Workflow Systems

Autonomous agent architectures using ReAct, MRKL, and Plan-and-Execute reasoning patterns with persistent memory, tool calling, human-in-the-loop approval gates, and multi-agent orchestration for complex enterprise workflows.

  • Tools: CrewAI, AutoGen, LangGraph multi-agent. Persistent memory via MemGPT and PostgreSQL. Tool registry with 200+ pre-built integrations. Structured output enforcement. HITL approval gates via Slack and email webhook. Task queue integration with Celery and BullMQ. Agent observability via Langfuse trace logging.
  • Case example: Insurance claims triage agent processing 800 claims per day autonomously, extracting documents, querying policy databases, generating adjustment recommendations, and escalating edge cases to human adjusters.

AI Data Pipeline Engineering

Real-time and batch data pipelines for model training, feature engineering, document ingestion, embedding generation, and vector store population. Processes millions of documents at enterprise scale with automated quality monitoring.

  • Tools: Apache Kafka for real-time streaming. Apache Airflow and Prefect for batch orchestration. dbt for transformation. Apache Spark for large-scale processing. Unstructured.io for document parsing. Automated PII detection via Microsoft Presidio. Data quality checks via Great Expectations. Embedding refresh triggers.
  • Case example: Healthcare intelligence platform ingesting 50,000 clinical documents daily with automated PII redaction, HIPAA classification, and embedding refresh cycle under 4 hours.

Model Fine-Tuning and Optimisation

Domain-specific model fine-tuning with LoRA, QLoRA, and full fine-tuning on A100/H100 GPU clusters. Model quantisation (INT4/INT8), pruning, and TensorRT optimisation for edge, mobile, and cost-constrained production deployments.

  • Tools: PEFT/LoRA and QLoRA fine-tuning on 7B to 70B parameter models. Full fine-tuning pipeline for sub-7B models. INT4 quantisation via GGUF and AWQ for 70 to 90 percent memory reduction. vLLM continuous batching serving at 400 to 1000 tokens per second. NVIDIA TensorRT-LLM optimisation. Under 50ms inference P99 for 7B models on A10G.
  • Case example: Legal contract AI fine-tuned on 200K proprietary contracts achieved 34% higher clause extraction F1 vs. GPT-4o base model at 82% lower inference cost via self-hosted quantised deployment.

AI Security Architecture

Comprehensive AI security covering prompt injection detection, output filtering, PII redaction, role-based AI access control, jailbreak testing, and complete audit logging. Meets OWASP LLM Top 10 and enterprise security standards.

  • Tools: OWASP LLM Top 10 hardening. Guardrails AI and NeMo Guardrails output validation. Llama Guard content classification. Input length limits and format validation. Prompt injection pattern matching plus ML classifier. Role-based AI feature access via JWT and API key dual-auth. Immutable audit log for every prompt, response, model, user, cost, and latency. PII redaction via Presidio before context injection.
  • Case example: Customer-facing financial AI advisor with 4 prompt injection vectors identified before launch. Zero security incidents in 14 months of production operation.

MLOps and AI Product Observability

Production AI observability platform covering hallucination detection, response quality scoring, latency and cost dashboards, semantic drift alerts, automated retraining triggers, and A/B testing infrastructure.

  • Tools: LangSmith and Langfuse trace logging. Helicone cost and latency analytics. RAGAS online hallucination scoring pipeline. Semantic drift detection via embedding distribution monitoring. Automated retraining trigger on drift threshold breach. Prompt A/B testing with statistical significance. PagerDuty alerting on quality regression. Grafana dashboards for real-time P50/P95/P99 latency and cost per query.
  • Case example: SaaS AI writing assistant where MLOps platform caught quality regression within 2 hours of a bad GPT-4o update. Automated rollback to previous prompt version with zero customer escalations.

AI Mobile Application Engineering

React Native and native iOS/Android AI-powered applications with on-device ML inference, real-time AI features, background processing, and seamless cloud model integration for latency-sensitive and offline use cases.

  • Tools: React Native (Expo), Swift, Kotlin. On-device inference via Core ML (iOS), TensorFlow Lite, ONNX Runtime (Android). Offline-capable AI features with local model caching. WebRTC for real-time voice AI. Under 200ms perceived UI response. Push notification triggers for async AI completion. App Store and Play Store compliance for AI features.
  • Case example: Retail mobile app with on-device product recognition (Core ML, offline) and cloud LLM product Q&A. 4.8 star App Store rating. 65% DAU engagement with AI features.

AI SaaS Platform Engineering

Multi-tenant AI SaaS architecture with per-tenant model customisation, knowledge base isolation, usage metering, rate limiting, enterprise SSO, and consumption-based billing. Built to scale from 10 to 100,000 enterprise tenants.

  • Tools: Microservices on Kubernetes (GKE, EKS, AKS). Per-tenant vector namespace isolation via Pinecone namespaces and Weaviate multi-tenancy. Tenant-specific prompt configuration DB. LoRA adapter hot-swapping for per-tenant fine-tuning. Stripe metered billing API. Auth0 and Okta SAML SSO. Rate limiting per tenant via token bucket with Redis. Usage analytics dashboard per tenant.
  • Case example: B2B AI document intelligence SaaS scaling from 50 to 3,200 enterprise tenants in 18 months. Zero cross-tenant data leakage. 99.97% uptime. $0.08 per document processing cost.

Enterprise System AI Integration

Deep embedding of AI capabilities into Salesforce, SAP, Microsoft 365, ServiceNow, Oracle ERP, and custom enterprise systems. AI at the point of work, not in a separate tool requiring context switching.

  • Tools: Salesforce Einstein and Apex integration. SAP BTP extension framework. Microsoft 365 Copilot Studio including Teams bots, Outlook add-ins, and Word/Excel plugins. ServiceNow Flow Designer AI integration. GraphQL, REST, and webhook patterns. OAuth 2.0 and SAML enterprise auth. Enterprise data loss prevention policy enforcement at integration layer.
  • Case example: Salesforce-embedded AI sales copilot generating deal summaries and next-best-action recommendations. 3x rep productivity. 55% faster deal cycles. Deployed to 1,200 sellers in 6 weeks.

AI Inference Cost Optimisation

Systematic reduction of GenAI API spend through intelligent model routing, semantic caching, prompt compression, context window management, and batch processing. Achieves 60 to 80 percent cost reduction without measurable quality degradation.

  • Tools: LiteLLM proxy with intelligent routing rules via query complexity classifier to model tier. Semantic cache with Redis and cosine similarity threshold at 30 to 50 percent cache hit rate. Prompt compression via LLMLingua at 40 to 60 percent token reduction. Context window trimming via summarisation of old turns. Async batching for non-real-time workloads. Per-model cost budget controls with circuit breakers. Monthly cost attribution dashboard by feature and user segment.
  • Case example: High-growth SaaS platform reducing monthly LLM API costs from $420K to $163K. $3.1M annual savings validated over 6-month measurement period with no quality regression on evaluation set.

Computer Vision Product Engineering

Production computer vision for defect detection, document OCR, video analytics, medical imaging analysis, and real-time object tracking. Deployed to cloud, GPU edge nodes, and embedded hardware.

  • Stack: YOLOv9, RT-DETR, SAM 2, ViT, CLIP, LayoutLMv3. TensorRT optimisation for NVIDIA Jetson edge at 10ms inference. PatchCore anomaly detection. Under 50ms P99 cloud inference.
  • Production result: Manufacturing visual QC system processing 10,000 PCBs per hour at 99.7% defect detection accuracy, replacing 14 manual inspectors. $2.8M annual cost saving.

Voice and Conversational AI Engineering

Real-time voice AI with custom STT/TTS pipelines, emotion and intent detection, speaker diarisation, and sub-500ms end-to-end latency. Built for call centres, IVR replacement, voice-first applications, and real-time meeting intelligence.

  • Stack: Whisper large-v3, AssemblyAI, ElevenLabs, Azure Cognitive TTS. WebRTC audio streaming. Deepgram real-time transcription under 300ms. Speaker diarisation via pyannote.audio. Twilio and Amazon Connect telephony integration.
  • Production result: Insurance contact centre voice AI handling 68% of inbound calls autonomously. $4.8M annual agent cost savings. CSAT improved from 3.4 to 4.2 out of 5.

AI API and Developer SDK Engineering

Production AI APIs and developer SDKs with OpenAPI documentation, intelligent rate limiting, API key management, versioning, developer portals, and real-time streaming. Enables third-party integrations at enterprise scale.

  • Stack: OpenAPI 3.1. TypeScript, Python, and Go SDK generation. Server-Sent Events for streaming. GraphQL subscriptions for real-time AI. Kong and AWS API Gateway with WAF. API key and OAuth 2.0 dual auth. Webhook delivery with retry logic.
  • Production result: AI API platform serving 180 enterprise integration partners at 99.98% availability and under 50ms gateway P95 latency. Developer onboarding reduced from 5 days to 4 hours via docs portal.

Multimodal AI Product Engineering

AI products combining text, image, audio, video, and structured data inputs. Enables AI document analysis with image extraction, video intelligence platforms, and multimodal customer service agents that see, hear, and respond.

  • Stack: GPT-4o, Gemini 1.5 Pro, Claude 3.5 multimodal APIs. LLaVA and Phi-3 Vision for on-premises vision LLM. PDF and document OCR with image preservation via LlamaParse. CLIP embeddings for cross-modal image search.
  • Production result: Property inspection AI processing photos, floor plans, and written reports simultaneously, generating structured damage assessments in under 45 seconds vs. a 4-hour manual process.

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Technical Architecture

Product Foundry Reference Architecture

A 6-layer AI product architecture ensuring every system is secure, observable, cost-efficient, and maintainable from launch to 100x scale. Each layer is independently scalable.

System Architecture
01
Data and Knowledge Foundation

Structured and unstructured data ingestion, processing, and knowledge storage.

Data Lake / Lakehouse (Databricks / Snowflake)
ETL / ELT Pipelines (Airflow, dbt)
Vector Store
Feature Store (Feast)
Document Ingestion (Unstructured.io, LlamaParse)
Real-time Event Streams (Kafka)
02
Model and Intelligence Registry

Foundation models, fine-tuning, versioning, and evaluation.

Foundation Model Registry (MLflow)
Fine-tuned Domain Models (LoRA, QLoRA)
Embedding Model Registry
Evaluation Harness (RAGAS, custom benchmarks)
Prompt Registry & Versioning (PromptLayer)
Model A/B Test Infrastructure
03
Orchestration and Agent Engine

LLM orchestration, agent workflows, memory, and tool calling.

LangChain / LangGraph Orchestration
Agent Tool Registry (200+ integrations)
Persistent Memory Systems (MemGPT, PostgreSQL)
RAG Retrieval Pipeline
Multi-model Intelligent Router
Human-in-the-Loop Approval Gates
04
Serving and Cost Infrastructure

High-performance inference, auto-scaling, and cost controls.

vLLM / NVIDIA Triton Inference Server
Semantic Cache (Redis + cosine similarity)
Auto-scaling (Kubernetes HPA / KEDA)
LiteLLM Intelligent Model Router
Rate Limiter (per-user, per-tenant)
Cost Budget Controls & Circuit Breakers
05
Application and Integration Layer

User-facing products and enterprise system connectors.

Web Application (React / Next.js)
Mobile App (React Native / Swift / Kotlin)
REST / GraphQL / SSE API
Enterprise Connectors (Salesforce, SAP, M365)
Developer SDK (TypeScript, Python, Go)
Admin Dashboard & Tenant Management
06
Observability and Governance

Monitoring, compliance documentation, and continuous improvement.

LLMOps Dashboard (Langfuse, Helicone)
Hallucination Monitor (online RAGAS scoring)
Cost Analytics
Immutable Audit Log
Semantic Drift Detection & Retraining Triggers
EU AI Act Technical Documentation Generator
  • Integrations & Partners
  • LLM Providers
OpenAI GPT-4o / GPT-4o-mini
Anthropic Claude 3.5 Sonnet
Google Gemini 1.5 Pro
Meta Llama 3.1 (self-hosted 70B / 405B)
Mistral Large
Cohere Command R+
  • Vector & Data Infrastructure
Pinecone
Weaviate
Qdrant
PostgreSQL pgvector
Snowflake Cortex
Databricks Vector Search
  • Cloud & Serving Infrastructure
AWS (SageMaker, Lambda, EKS)
Google Cloud (Vertex AI, GKE)
Azure (OpenAI Service, AKS)
NVIDIA Triton Inference Server
vLLM Self-Hosted Serving
Private GPU Clusters (A100 / H100)
  • Enterprise Systems
Salesforce (Einstein, Apex)
SAP S/4HANA / BTP
Microsoft 365 / Copilot Studio
ServiceNow Flow Designer
Oracle ERP
Slack / Microsoft Teams
Technology Stack

AI Frameworks and Libraries

AI Frameworks & Libraries

Python
PyTorch
TensorFlow
JAX
Hugging Face
LangChain
LlamaIndex
AutoGen
CrewAI
OpenAI API
Anthropic Claude
Google Gemini

ML Infrastructure & Cloud

AWS SageMaker
Google Vertex AI
Azure OpenAI
Pinecone
Weaviate
Qdrant
Redis
Kafka
Kubernetes
MLflow

Foundation LLM Models

GPT-4o
Claude 3.5 Sonnet
Llama 3.1 70B
Mistral Large
Gemini 1.5 Pro
Cohere Command R+
Whisper
DALL·E 3 Contract

Business Integrations

Salesforce CRM
HubSpot CRM
Zendesk Support
ServiceNow ITSM
Microsoft 365 Productivity
Google Workspace Productivity
Slack Communication
Jira Project Mgmt
SAP ERP
Snowflake Data Warehouse
Databricks Data Platform
Stripe Payments

42+ technologies integrated

Our Process

Product Foundry Delivery Process

Step 1 Week 1

Product Brief and Technical Discovery

Translate your product vision into a technical architecture specification. Define AI capabilities, data requirements, integration touchpoints, success KPIs, and compliance requirements before writing any code.

Deliverables
AI Product Technical Specification 6-layer Architecture Design Document Data Requirements and Quality Audit Enterprise Integration Map Success KPI Framework EU AI Act Risk Classification
Step 2 Weeks 2 to 3

Data Architecture and Knowledge Pipeline

Design and build the data foundation: ingestion pipelines, vector store configuration, embedding strategies, feature engineering, and evaluation datasets.

Deliverables
Data Pipeline Architecture Vector Store Configuration and Namespace Design Evaluation Dataset (500+ examples) Feature Store Schema Data Quality Monitoring Config
Step 3 Weeks 3 to 7

AI Core Development

Model fine-tuning or RAG pipeline construction, agent workflow development, prompt engineering, and evaluation-driven iteration. Produces the AI intelligence layer benchmarked against your domain requirements.

Deliverables
Fine-tuned or Configured AI Model RAG Pipeline with RAGAS Evaluation Report Agent Workflow Specification Prompt Registry (versioned) Domain Benchmark Quality Report
Step 4 Weeks 5 to 9

Product Application Build

User-facing product: web application, mobile app, API, or enterprise integration. Includes streaming AI responses, real-time feedback, AI-native UX patterns, and admin dashboard for product team management.

Deliverables
Web or Mobile Application (staging environment) REST/GraphQL/SSE API Enterprise Integration Connectors Admin Dashboard API Documentation (OpenAPI)
Step 5 Weeks 8 to 10

Infrastructure and Cost Optimisation

Production inference infrastructure with vLLM serving, semantic caching, intelligent model routing, auto-scaling, and cost controls. Documented unit economics showing cost per query at target scale.

Deliverables
Production Inference Infrastructure Semantic Cache Layer (Redis) Intelligent Model Router Configuration Auto-scaling Rules and Load Test Report Cost Model: Cost per Query at 1K, 10K, 100K DAU
Step 6 Weeks 9 to 11

Security Audit and Compliance Clearance

OWASP LLM Top 10 hardening, prompt injection penetration testing, PII audit, GDPR data flow documentation, and EU AI Act risk assessment before production go-live.

Deliverables
OWASP LLM Top 10 Security Audit Report Prompt Injection Penetration Test Results PII Audit and Redaction Configuration GDPR Data Flow Map EU AI Act Risk Assessment and Classification
Step 7 Weeks 11 to 16

Production Launch and MLOps Handover

Go-live deployment, monitoring dashboard activation, runbook documentation, retraining schedule, and 90-day hypercare with weekly quality reviews and under 4-hour incident response SLA.

Deliverables
Production Deployment (canary rollout) MLOps Monitoring Dashboard (Langfuse, Grafana) Runbook and Incident Response Documentation Team Enablement Sessions (3 x 2 hours) 90-Day Hypercare SLA Agreement
Total: 8 to 16 weeks from brief to production deployment
Compliance & Regulatory

AI Product Compliance and Governance

  • Global Jurisdiction Coverage
🇪🇺

European Union

EU AI Act

GDPR

AI Liability Directive

🇺🇸

United States

NIST AI RMF

Executive Order on AI

CCPA

🇬🇧

United Kingdom

UK AI Regulation

ICO Guidance

CDEI

🇸🇬

Singapore

MAS AI Guidelines

PDPA

Model AI Governance

🇦🇪

UAE

UAE AI Strategy

PDPL

TDRA

🇨🇦

Canada

AIDA

PIPEDA

OSFI Guidelines

🇦🇺

Australia

AI Ethics Framework

Privacy Act

APRA

  • Certifications

ISO/IEC 42001

AI management system

SOC 2 Type II

Security & confidentiality

ISO 27001

Information security

GDPR Compliant

Security & availability controls

OWASP Hardened

LLM security standards

HIPAA Ready

Healthcare AI compliance

  • Regulatory Frameworks We Support

EU AI Act

Risk-based AI regulation - High-Risk AI system requirements

NIST AI RMF

NIST Artificial Intelligence Risk Management Framework

ISO/IEC 42001

International AI management system standard

GDPR Art. 22

Automated decision-making and profiling protections

SOC 2 Type II

Security, availability & confidentiality for AI systems

OWASP LLM Top 10

Security risks for large language model applications

CDEI AI Governance

UK Centre for Data Ethics & Innovation guidance

MAS AI Guidelines

Singapore MAS Fairness, Ethics, Accountability guidance

Security & Audit

AI Product Security Architecture

Production AI products face a unique threat surface: prompt injection, data exfiltration via RAG, jailbreak attacks, PII leakage, and model inversion. Ment Tech Labs applies defence-in-depth across every layer of the AI product stack.

  • Audit Partners

Trail of Bits

AI/ML security assessments

HiddenLayer

AI model security platform

Robust Intelligence

AI risk management

BishopFox

AI red teaming services

NCC Group

Enterprise AI security

Cure53

LLM API security testing

  • Security Certifications

OSCP

CISSP

GREM (Reverse Engineering)

AWS Security Specialty

ISO 27001 LA

  • Security Features

Prompt injection detection & prevention

LLM output filtering and content moderation

Hardware security modules (HSM)

PII detection & automatic redaction

Hallucination detection & confidence scoring

Rate limiting & abuse prevention

Audit logging for all AI interactions

Model versioning & rollback capability

Adversarial input detection

Data residency & sovereignty controls

End-to-end encryption for sensitive prompts

Human-in-the-loop escalation workflows

Enterprise-Grade Security

Bank-level encryption and compliance standards

256-bit AES Encryption

99.99% Uptime SLA

24/7 Monitoring

Industry Applications

AI Products Shipped Across Industries

Legal Tech

AI-Powered Legal Research Platform

RAG-powered legal research product indexing 10M+ case law documents with semantic search, jurisdiction filtering, citation chain verification, and AI-generated brief summaries.

75% attorney research time reduction

10M+ documents indexed across 6 jurisdictions

99.1% citation accuracy

Under 95ms retrieval P95 latency

SaaS and Technology

Enterprise AI Sales Copilot

Salesforce-embedded AI copilot generating deal health summaries, next-best-action recommendations, competitor battle cards, and personalised outreach drafts inside the CRM.

3x rep productivity increase

55% faster deal cycle time

40% pipeline coverage improvement

Deployed to 1,200 sellers in 6 weeks

Healthcare

Clinical Document Intelligence Platform

HIPAA-compliant AI platform extracting structured data from unstructured clinical notes, radiology reports, and discharge summaries for clinical trials and quality reporting.

10x faster structured data extraction

98.5% extraction F1 score

HIPAA-compliant architecture, zero PHI in logs

50,000 documents processed per 24-hour cycle

Asset Management

AI Financial Analysis Engine

Multi-source financial intelligence platform ingesting earnings calls, SEC filings, analyst reports, and news. Generates AI-powered equity research summaries for portfolio managers supporting $2.4B AUM.

80% faster earnings analysis workflow

$2.4B AUM supported

SEC filing processed to summary in under 30 seconds

4.9 out of 5 portfolio manager satisfaction score

Manufacturing

Computer Vision QC System

Real-time computer vision quality inspection processing 10,000 PCBs per hour at 99.7% defect detection accuracy. Edge deployment on production floor.

99.7% defect detection accuracy

10,000 units per hour at under 50ms inference

98% reduction in field escape incidents

$2.8M annual cost saving

E-commerce and Retail

AI Customer Experience Platform

Omnichannel AI platform handling 85% of customer interactions autonomously across web chat, mobile, email, and WhatsApp with seamless CRM-synced human escalation and support for 12 languages.

85% autonomous resolution rate

4.7 out of 5 post-interaction CSAT

24/7 coverage across 12 languages

1.2 second response time vs. 4.5 minute human average

See Our AI Solutions in Action

Get a personalized live demo tailored to your exact use case - built by the same engineers who will work on your project.

  • 30-min technical deep-dive
  • Custom to your use case
  • Meet your future team
Request a Live Platform Demo

Comparison

Custom AI Product vs. SaaS AI Platform vs.
In-House Build

Why traditional security tools miss AI-specific attack vectors.

Decision Dimension
RECOMMENDED Custom AI Product (Ment Tech Labs)
SaaS AI Platform
In-House AI Build
Proprietary Data Protection
Full control, data never leaves your environment
Data sent to SaaS vendor, DPA required
Full control, but requires internal infrastructure
Customisation Depth
Unlimited: fine-tuning, custom architecture, domain models
Limited to vendor feature set
Unlimited, but requires scarce ML engineering talent
Time to Production
8 to 16 weeks
2 to 8 weeks (configuration only)
6 to 24 months
Inference Cost at Scale
60 to 80% optimised via routing, caching, self-hosted models
Vendor margin included, costs scale linearly
Controllable, but requires dedicated MLOps team
EU AI Act Compliance
Full control, architecture designed for compliance
Dependent on vendor compliance roadmap
Full control, but internal legal expertise required
Competitive Differentiation
High: unique AI capabilities become proprietary moat
Zero: competitors access same vendor AI capabilities
High, but 12 to 24 month execution risk
Ongoing Engineering Cost
Engineering retainer for iteration and MLOps: $15K to $80K/month
SaaS subscription: $50K to $500K/year
$1M to $4M/year for 5-person AI engineering team
Ideal Company Stage
Series A+ to enterprise
Early-stage or SME needing fast generic AI features
Well-funded enterprise with 12+ month runway and AI talent pipeline

Our Recommendation

Custom AI product engineering is the optimal choice when AI capability is a primary competitive differentiator, proprietary data is involved, or inference costs at scale make SaaS platforms economically unviable.

Get Started
Case Study

FinTech Startup Ships AI Document Platform in 10 Weeks and Closes £8.5M Series A

FinTech Startup (Pre-Series A)

Financial Technology

The Challenge

A London-based FinTech startup needed a production AI-powered financial document intelligence platform to compete for Series A. They had 10 weeks, zero in-house AI engineers, a board requiring a live product, and a CFO questioning whether AI was defensible IP or just an OpenAI wrapper.

Our Solution

Ment Tech Labs deployed a 5-person AI product engineering team. We built a RAG-powered financial document analysis platform with GPT-4o, custom fine-tuning on 50K proprietary financial documents, Pinecone vector store, React web application with streaming AI responses, full OWASP LLM security hardening, and a production MLOps monitoring stack. Delivered in 10 weeks. The custom fine-tuned model achieved 34% higher extraction accuracy than GPT-4o base, creating defensible IP called out specifically in Series A investor diligence.

  • Results Achieved

10 weeks vs 12-month in-house estimate by CTO

Time to Production

98.7% +34% vs GPT-4o base model

Financial Document Extraction Accuracy

£8.5M AI product cited as primary differentiator in term sheet

Series A Closed

£0.0012 vs £0.0089 naive GPT-4 (87% cost reduction)

Inference Cost per Document

Zero findings Clean security audit before investor diligence

OWASP LLM Top 10

Ment Tech built the product that got us funded. Their AI engineering depth was years ahead of any agency we spoke to - they shipped things we didn't even know were possible in the timeframe, and the investor diligence team came back saying the AI was genuinely proprietary, not a ChatGPT wrapper."
Founder & CEO
FinTech Startup, London at NDA - Financial Technology

ROI & Value

AI Product Engineering ROI

Key Metrics

  • Inference Cost vs. Naive Integration
vs. reduction through intelligent routing and caching
0 -80%
  • Faster Time to Production
vs. vs. typical in-house AI product builds
0 -5×
  • Post-Launch Failure Rate
vs. vs. 45% industry average for AI products (VentureBeat)
< 0 %
  • Year-1 Revenue Impact
vs. depending on product category and user scale
$ 0 -15M
  • Cost Savings Breakdown

Inference Cost Engineering

Model routing, semantic caching, prompt compression, and self-hosted models reducing API spend.

$200K to $3M per year

Faster Time to Market

Revenue captured 6 to 12 months earlier than typical in-house builds.

$500K to $5M

Avoided Post-Launch Rebuild

Production-first architecture prevents the 60% of AI products that require architectural rewrites within 6 months of launch.

$300K to $2M

AI Engineering Team Cost Avoidance

vs. hiring a 5-person in-house AI engineering team at $200K to $800K per engineer fully loaded.

$1M to $4M per year

Security and Compliance Avoidance

Proactive EU AI Act compliance and OWASP LLM hardening preventing regulatory fines and reputational incidents.

$500K to $5M

Potential Annual Savings

Up to 70%

Engagement Models

AI Agent Security Engagement Models

AI Product Sprint

4 to 6 week intensive engagement. Design and build a working, demonstrable AI product MVP validated with real users. Suitable for funding milestones, innovation labs, and de-risking technical feasibility.

Ideal for

Pre-seed to Series A startups, enterprise innovation labs, and teams validating a new AI product concept before full investment commitment.

  • Deliverables: Product architecture design
  • Core AI capability build (LLM/RAG/fine-tuning)
  • Basic web or API interface
  • Evaluation framework and quality report
  • OWASP LLM basic security scan
  • Go/no-go production readiness report
  • Delivery: 4-6 weeks

Full Product Engineering

8 to 16 week end-to-end build. Production AI product with enterprise integrations, inference cost optimisation, MLOps monitoring, security audit, compliance clearance, and 90-day hypercare.

Ideal for

Enterprises launching AI-native products, Series A/B startups building differentiated AI capabilities, or teams replacing failed in-house AI builds.

  • Deliverables: Complete 7-phase delivery
  • Enterprise system integrations (Salesforce, SAP, M365)
  • Inference cost optimisation (60 to 80% reduction target)
  • MLOps monitoring and alerting infrastructure
  • Security audit (OWASP LLM Top 10 + penetration test)
  • EU AI Act risk assessment and documentation
  • EU AI Act risk assessment and documentation
  • 90-day hypercare with weekly quality reviews
  • Delivery: 8 to 16 weeks

AI Engineering Partnership

Embedded AI engineering team extending your capability for continuous product iteration. Dedicated senior AI engineers working inside your team under your technical leadership.

Ideal for

Post-launch companies scaling AI products, enterprises augmenting in-house teams, and organisations building permanent AI product capabilities.

  • Deliverables: 2 to 5 dedicated senior AI engineers
  • Direct backlog contribution under client technical leadership
  • Architecture governance and review
  • Continuous model and prompt optimisation
  • Regulatory compliance tracking
  • Team upskilling and knowledge transfer
  • Strategic AI product roadmap input
  • Duration: 6 to 24 months typical

What is Included in Every Engagement

  • Technical architecture design and specification
  • LLM integration, fine-tuning, and prompt engineering
  • RAG pipeline engineering with RAGAS evaluation
  • REST/GraphQL API development and documentation
  • OWASP LLM Top 10 security hardening
  • EU AI Act risk classification and documentation
  • MLOps monitoring dashboard (Langfuse, Grafana)
  • Inference cost optimisation (routing and caching)
  • IP assignment agreement (100% client ownership)
  • 90-day post-launch hypercare support

Get Your Tailored Project Quote

Share your requirements and receive a detailed technical proposal with transparent pricing within 48 business hours.

  • Transparent pricing, no surprises
  • Proposal in 48 hours
  • Flexible engagement models
Get a Custom Project Quote
FAQ

Frequently Asked Questions

AI consulting produces strategy documents. AI development produces model artefacts. AI product engineering produces a shippable, production-ready software product used daily by real customers, with monitoring and continuous improvement built in.
8 to 16 weeks with Ment Tech Labs. Most in-house teams estimate 6 to 24 months due to hiring, onboarding, and integration cycles.
Architecture depends on the use case. RAG is best for updatable knowledge retrieval. Fine-tuning is best for format consistency and domain specialisation. Most enterprise products benefit from a combination of both.
Build custom when AI is a primary competitive differentiator, proprietary data is involved, or inference volume exceeds $30K per month in API spend.
Intelligent model routing, semantic caching with Redis, prompt compression via LLMLingua, context window trimming, and async batching for non-real-time workloads. Combined, these achieve 60 to 80% cost reduction.
Yes. We have production integrations across Salesforce Einstein, SAP BTP, and Microsoft 365 Copilot Studio including Teams bots, Outlook add-ins, and Word/Excel plugins.
OWASP LLM Top 10 hardening at the API gateway layer, ML-based prompt injection detection on every incoming request, Guardrails AI output validation, and immutable audit logging of every AI interaction.
Yes. EU AI Act risk classification, technical documentation generation, and high-risk AI system requirements are included in every engagement from architecture design onwards.
Yes. We support full on-premises deployment with self-hosted LLMs (Llama 3.1 70B and 405B), private Kubernetes clusters, and private vector store instances with no external API dependencies.
Per-tenant vector namespace isolation in Pinecone and Weaviate, tenant-specific prompt configuration databases, LoRA adapter hot-swapping for per-tenant fine-tuning, and Redis token bucket rate limiting per tenant.
100% of IP transfers to the client. This includes code, fine-tuned model weights, prompt libraries, and evaluation datasets. Zero royalties or revenue share.
Langfuse and Grafana dashboards, online RAGAS hallucination scoring, semantic drift detection, automated retraining triggers, prompt A/B testing, and 90-day hypercare with weekly quality reviews and under 4-hour incident response SLA.
RAG for updatable knowledge retrieval where the knowledge base changes frequently. Fine-tuning for format consistency, domain-specific terminology, and cases where retrieval alone does not achieve required accuracy. Best results come from combining both.
Production-first architecture from day one. Auto-scaling infrastructure, inference cost controls, evaluation harnesses, drift monitoring, and security hardening designed in before the first line of application code is written.
Yes. The AI Engineering Partnership model embeds 2 to 5 dedicated senior AI engineers directly into your backlog under your technical leadership, typically onboarded within 2 weeks.

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Summary

Key Takeaways

  • AI product engineering delivers a complete, production-ready AI software product, not a model artefact, strategy document, or POC demo.
  • 87% of AI projects fail before production. The failure mode is engineering and architecture, not model quality.
  • Ment Tech Labs ships production AI products in 8 to 16 weeks with 60 to 80% inference cost reduction vs. naive LLM integration.
  • Build custom AI products when AI capability is a strategic differentiator, proprietary data is involved, or inference volume exceeds $30K per month in API spend.
  • Multi-tenant AI SaaS architecture with namespace isolation enables B2B AI products that scale from 10 to 50,000+ enterprise tenants with zero data leakage risk.
  • RAG and fine-tuning solve different problems and are best combined: RAG for updatable knowledge retrieval, fine-tuning for format consistency and domain specialisation.
  • 100% IP ownership including fine-tuned model weights, prompt libraries, and evaluation datasets transfers to the client. Zero royalties or revenue share.
  • EU AI Act compliance for high-risk AI systems is required by August 2026. Products deployed in hiring, credit, healthcare, or critical infrastructure must begin compliance architecture now.
  • MLOps monitoring is not optional. Semantic drift, hallucination rate tracking, and automated retraining are required for AI products to improve over time rather than silently degrade.
  • AI engineering team augmentation delivers staff-plus level AI engineers directly into your backlog in weeks vs. a 6 to 9 month typical in-house hire cycle.
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Ready to Build Your AI Product?

From product brief to production deployment in 8 to 16 weeks. Ment Tech Labs provides the complete AI engineering stack: LLM integration, RAG pipelines, MLOps, security hardening, and the application layer. You ship a real product, not a demo. 200+ AI products shipped. 100% IP ownership transferred.

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  • ISO/IEC 42001 Certified
  • SOC 2 Type II
  • EU AI Act Compliant
  • 200+ AI Products Shipped
  • OWASP LLM Hardened
  • 100% IP Ownership
4.9 / 5.0 from 100+ client reviews

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