Production Architecture & Hardening
We strengthen the architecture, APIs, queues, databases, retries, and failure handling required for a reliable POC to production transition.
We strengthen the architecture, APIs, queues, databases, retries, and failure handling required for a reliable POC to production transition.
We connect your pilot with live CRMs, ERPs, internal APIs, data pipelines, vector stores, and authentication systems.
We build evaluation pipelines for accuracy, latency, hallucinations, edge cases, and task performance before moving AI POC development into production.
We track model outputs, traces, token usage, latency, errors, and infrastructure health so issues are visible before they affect users.
We add access controls, audit logs, human review, and data safeguards, supported by AI consulting services where stronger governance is required.
We set up CI/CD, model and prompt versioning, rollback workflows, and release controls to move AI MVP development into a maintainable production environment.
7+ Years
Industry Experience
90 Days
Production Sprint
Week 3
Working Agent
30 Days
Post-Launch Stabilization
6-Step
Production Process
Monoskin Pharma
JD Homes
Wealth Management Platform
We think beyond the demo from day one. Reliability, failure handling, monitoring, security, and operating cost are treated as part of the build, not as cleanup work later.
We keep the scope tight around one workflow, one business outcome, and the systems it depends on. That makes it easier to test properly and move faster without losing control.
We strengthen the parts that usually break first in production, including APIs, data pipelines, queues, routing, caching, and infrastructure.
Senior engineers remain engaged in all critical decisions, including architecture, integrations, risks to production, scheduling releases, and more.
Your AI pilot-to-production setup stays in your environment, under your accounts and access rules. Your code, data flows, configurations, and IP remain yours.
We make sure your team knows how the system works, what to watch, and what to do when something goes wrong, with runbooks, monitoring, and clear operating ownership in place.
We agree on the workflow, success metric, production load, risk limits, and launch requirements before development begins. Our AI consulting services can also help validate whether the use case is ready to move forward.
We analyze the current architecture, models, prompts, APIs, data flows, and integrations to determine if they're usable as is or if they need modification.
We strengthen the data pipelines, APIs, authentication, infrastructure, and system integrations required to move from prototype to production reliably.
We define measurable checks for quality, latency, cost, security, and edge cases so every release has a clear pass or fail decision.
We add monitoring, retries, fallbacks, access controls, audit logs, versioning, rollback paths, and human review where the workflow needs it.
We deploy in your environment, validate live performance, train the internal owner, and support the system through the POC to production handover and stabilization period.
We concentrate on one workflow, moving it from proof of concept to production and validating it against real business, security, and operational requirements before scaling.
If your pilot works but still isn’t ready for real users, live data, or enterprise systems, we’ll identify what’s blocking deployment and map the fastest path to production.
We review what is currently functioning, find the loopholes in architecture, integrations, evaluation, security, and monitoring, and safeguard the architecture from the existing prototype to the production environment without unnecessary rebuilding.
A pilot is operational/ready when it has achieved consistency in delivering under agreed targets for quality, latency, cost, reliability, and security on real or representative data.
Not always. We retain useful models, prompts, APIs, and workflows, then refactor only the parts that cannot support production. Our AI POC development services can help where bigger changes are needed.
For a well-scoped workflow, our target is a 90-day production cycle. The timeline depends on integration complexity, data readiness, security, and the current state of the pilot.
Production requires stronger evaluation, observability, access controls, rollback workflows, cost management, and live integrations beyond what most AI MVP development services include at the MVP stage.
We add access controls, audit logs, data safeguards, human review points, and traceability before go-live. For higher-risk use cases, responsible AI governance is built into the workflow.
Your team does. We provide documentation, monitoring, runbooks, and knowledge transfer so your internal team can operate it, with custom AI development services available if further support is needed.
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.