Enterprise Copilots & Assistants
Copilots embedded in the tools your teams already use, grounded in your own data, with permission-aware retrieval so each user only ever sees what they are entitled to.
Ment Tech Labs is an AI product engineering company that takes AI from a working demo to production software. We own the full stack, from LLM and retrieval architecture to evaluation harnesses, cost and latency budgets, and the application layer around them, so your AI product ships with the accuracy, reliability, and controls enterprise users expect, backed by production-grade AI agent development.
We build the parts that decide whether an AI product survives real users: retrieval that cites its sources, tools that are safe to retry, evaluations that catch regressions before release, and a cost per request you can forecast.
Product Discovery & Solution Architecture
We scope the use case, pick the model and retrieval approach, settle build versus buy, and define the accuracy, latency, and cost targets the product has to hit before any code is written.
Custom LLM Application Development
Full-stack product engineering around the model: orchestration, prompt and context layer, APIs, web and mobile interfaces, and the state handling that keeps multi-step work resumable.
Data & Retrieval Engineering
Corpus-tuned chunking, hybrid search, reranking, and permission-aware retrieval, with every answer carrying the source passage it was grounded in.
Model Layer, Fine-Tuning & Routing
We benchmark your workload across models, fine-tune only where it pays for itself, and route requests by task so quality and cost per request are both under control.
Agentic Workflows & Tool Integration
Typed, idempotent, permissioned tools with bounded loops and human checkpoints on consequential actions, so the product can act without acting unpredictably.
Enterprise Integration & Deployment
We wire the product into the systems it has to live with, then ship it on CI/CD with staged rollouts, feature flags, and a rollback path that actually works.
Evaluation, Observability & Continuous Improvement
Golden datasets and regression suites in CI, traces across prompts, retrieved context and tool calls, drift alerts, and a feedback loop that turns real usage into the next release.
Every AI product fails in a different place: the retrieval layer, the tool calls, the cost per request, or the day the model changes underneath it. We engineer for those failure points from the first sprint. See how this fits into our wider AI development services.
Copilots embedded in the tools your teams already use, grounded in your own data, with permission-aware retrieval so each user only ever sees what they are entitled to.
Products that read contracts, claims, policies, and reports at scale, returning structured output with citations auditors and reviewers can actually check.
Products where the AI does the work rather than describing it: multi-step execution across your systems, with bounded tool use and human approval where it matters.
Multi-tenant SaaS built around a model from day one, with per-tenant data isolation, usage metering, and unit economics that still work at scale.
Products that score, forecast, and flag, combining models with your business rules so every output comes with a reason a reviewer can defend.
Chat and voice products engineered for real conversations: low latency, interruption handling, context that survives the session, and clean escalation to a human.
AI product engineering closes that gap with evaluation harnesses, cost and latency budgets, guardrails, and observability built in, so your product holds up against real users and real data.
Every industry now ships AI inside its core product. We build where accuracy, auditability, and cost per request decide whether the product is viable at all, from the trading desk to the factory floor.
Products touching payments, KYC, and customer records, built with audit-ready logging, deterministic business rules, and explainable output. See our AI agents for banking.
Clinical documentation and patient-facing products engineered with PHI-aware data handling, strict retrieval scoping, and clinician review built into the workflow.
Search, recommendation, and support products that have to answer in milliseconds at peak traffic, so caching and cost per request are engineered in, not patched later.
Quality inspection, maintenance, and planning products wired into MES and SCADA data, built to run reliably at the edge as well as in the cloud.
Planning and exception-handling products that act across TMS, WMS, and carrier APIs, with hard limits on what an automated decision is allowed to move.
Claims and underwriting products that extract structured data from messy documents and show the evidence behind every recommendation they make.
As an AI product engineering company, we own the whole product: the architecture, the model and retrieval layer, the application around it, and the operational tooling that keeps it accurate after launch.
One team owns the model layer, the retrieval layer, the application, and the deployment pipeline, so nothing falls into the gap between a data team and a product team.
We design for the load, the failure modes, and the audit trail from the first sprint, rather than retrofitting a prototype that was never meant to carry real traffic.
Your AI product has to live inside existing systems. We handle that through enterprise AI integration, from identity and permissions to legacy data sources.
Model routing, caching, batching, and token budgets per request, so your cost per user is a number you can forecast before the invoice arrives.
Guardrails, PII handling, access scoping, and decision logging engineered in from the start, mapped to your AI governance requirements.
Every release passes an evaluation suite before it ships, and real usage feeds the next one, so the product gets measurably better instead of quietly drifting.
Model choice drives accuracy, latency, and cost per request. We benchmark your actual workload across frontier and open-weight models, then architect for portability so you can switch providers later without rebuilding the product.
GPT-6
Claude Opus 5
Gemini 3.6
Llama 4
Mistral
Grok
We build on the stack your product will run on: LLM and agent frameworks, vector and data infrastructure, cloud and container platforms, CI/CD and code quality tooling, and the observability and guardrail layer that keeps it reliable in production.
An eight to twelve week engagement, from product scoping and architecture through evaluation, a measurable production release, and the improvement loop that follows it.
Weeks 1-2. We pin down the use case, the model and retrieval approach, the integration surface, and the accuracy, latency, and cost targets the product must hit to be worth shipping.
Weeks 2-4. We prepare your corpus, build the retrieval pipeline with permission-aware scoping, and create the golden dataset every later release is measured against.
Weeks 3-8. Orchestration, prompt and context engineering, tool integration, APIs, and the application interface, built in two-week increments you can review as they land.
Weeks 8-10. Regression suites in CI, adversarial and edge-case testing, guardrail tuning, load testing, and cost and latency optimization against the targets set in week one.
Weeks 10-12. Staged rollout behind feature flags, monitoring and alerting live from day one, runbooks, and handover sessions for the teams who will own it.
Models change, data drifts, and usage patterns move. We monitor quality, cost, and latency in production and ship improvements on a regular cadence. Our AI pilot to production services keep the product stable as it scales.
Explore the related services that help you govern, scale, and keep improving your AI product after the first release.
We design and build the agents inside your product with typed tools, bounded loops, and human checkpoints engineered in from the first sprint.
We align your AI product with the EU AI Act, NIST AI RMF, and ISO 42001, turning model and data decisions into documented, auditable controls.
We take a validated pilot to company-wide rollout with monitoring, evaluation gates, and the operational ownership a live product needs.
Share your project goals, technical requirements, and current challenges with Ment Tech Labs. Our team will review your needs and recommend a clear, practical path from planning to production.
Prefer email? contact@ment.tech
