4.9 / 5.0 from 100+ client reviews
Trusted & 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
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
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.
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.
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.
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.
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.
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.
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
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Ready to Tokenize Your Assets?
Schedule a free 30-minute strategy call with our tokenization architects.
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.
Structured and unstructured data ingestion, processing, and knowledge storage.
Foundation models, fine-tuning, versioning, and evaluation.
LLM orchestration, agent workflows, memory, and tool calling.
High-performance inference, auto-scaling, and cost controls.
User-facing products and enterprise system connectors.
Monitoring, compliance documentation, and continuous improvement.
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
Business Integrations
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.
Design and build the data foundation: ingestion pipelines, vector store configuration, embedding strategies, feature engineering, and evaluation datasets.
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.
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.
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.
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.
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.
BSA (US)
Bank Secrecy Act suspicious activity and currency transaction reporting
AMLD6 (EU)
Sixth Anti Money Laundering Directive
FATF Recommendations
Global anti money laundering and counter terrorism finance
OFAC Sanctions
US Office of Foreign Assets Control sanctions screening
EU Sanctions
EU consolidated financial sanctions list
MAR / MiFID II
Market Abuse Regulation and Markets in Financial Instruments
DAC6
EU directive on cross border tax arrangement reporting
FATCA / CRS
Foreign Account Tax Compliance and Common Reporting Standard
Security & Audit
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.
AI/ML security assessments
AI model security platform
AI risk management
AI red teaming services
Enterprise AI security
LLM API security testing
OSCP
CISSP
GREM (Reverse Engineering)
AWS Security Specialty
ISO 27001 LA
Bank-level encryption and compliance standards
256-bit AES Encryption
99.99% Uptime SLA
24/7 Monitoring
Industry Applications
RAG-powered legal research product indexing 10M+ case law documents with semantic search, jurisdiction filtering, citation chain verification, and AI-generated brief summaries.
Salesforce-embedded AI copilot generating deal health summaries, next-best-action recommendations, competitor battle cards, and personalised outreach drafts inside the CRM.
HIPAA-compliant AI platform extracting structured data from unstructured clinical notes, radiology reports, and discharge summaries for clinical trials and quality reporting.
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.
Real-time computer vision quality inspection processing 10,000 PCBs per hour at 99.7% defect detection accuracy. Edge deployment on production floor.
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.
Get a personalized live demo tailored to your exact use case - built by the same engineers who will work on your project.
Comparison
Why traditional security tools miss AI-specific attack vectors.
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.
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.
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
ROI & Value
Model routing, semantic caching, prompt compression, and self-hosted models reducing API spend.
Revenue captured 6 to 12 months earlier than typical in-house builds.
Production-first architecture prevents the 60% of AI products that require architectural rewrites within 6 months of launch.
vs. hiring a 5-person in-house AI engineering team at $200K to $800K per engineer fully loaded.
Proactive EU AI Act compliance and OWASP LLM hardening preventing regulatory fines and reputational incidents.
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.
Pre-seed to Series A startups, enterprise innovation labs, and teams validating a new AI product concept before full investment commitment.
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.
Enterprises launching AI-native products, Series A/B startups building differentiated AI capabilities, or teams replacing failed in-house AI builds.
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.
Post-launch companies scaling AI products, enterprises augmenting in-house teams, and organisations building permanent AI product capabilities.
Share your requirements and receive a detailed technical proposal with transparent pricing within 48 business hours.
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.
Can't find the answer you're looking for? Our team is here to help.
Generative AI Development
Custom generative AI applications powered by GPT-4, Claude, and Gemini.
AI Agent Development
Autonomous AI agents that perceive, plan, and act across complex workflows.
LLM Development
Custom large language model development, fine-tuning, and deployment.
AI Chatbot Development
Conversational AI chatbots for customer service, sales, and internal support.
RAG Development
Retrieval-Augmented Generation systems for knowledge-grounded AI responses.
Machine Learning Development
Custom ML models for prediction, classification, and anomaly detection.
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.
+91-74798-66444
Contact@ment.tech
+91-74798-66444
