01 · Prototype audit
What stands between your POC and production.
Review existing AI proof-of-concepts for production readiness: hallucination risk, latency, cost per call, and failure modes.
LLM integration · MLOps · Production AI
From working prototype to production AI.
Most AI prototypes never ship. We take proof-of-concepts built on OpenAI, Anthropic, or open-source models and harden them into production systems — with proper error handling, cost controls, latency budgets, and monitoring.
Capabilities
Every layer a prototype skips to get to production.
Prototypes skip error handling, cost controls, and observability. We put each layer in place before the system handles real users and real load.
02 · LLM integration hardening
Retry logic, fallbacks, and structured output validation.
Wrap raw API calls in retry logic, fallback models, prompt versioning, and structured output validation.
03 · RAG pipeline build-out
Chunking, embedding, retrieval, and context management.
Connect your knowledge base to the model with chunking, embedding, retrieval tuning, and context window management.
04 · Cost and latency optimization
Token budgeting, caching, and model routing.
Token budgeting, prompt caching, model routing, and batching to bring per-request cost under control.
05 · Monitoring and observability
Trace every call, log inputs/outputs, alert on drift.
Trace every LLM call, log inputs/outputs, alert on quality drift, and track spend per feature.
06 · MLOps and deployment
Containerized serving and staged rollout.
Containerized model serving, CI/CD for prompt changes, and staged rollout for new model versions.
How we work
Four phases. Same team across all four.
The phases that apply to every engagement, not just ai prototype to production. The team that scopes does the building, and the operating.
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Phase 01 · 2–4 weeks
Discovery and scope.
Stakeholder interviews, technical review of existing systems, risk register, written scope with milestones and exit criteria.
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Phase 02 · 3–12 months
Build and iterate.
Two-week sprints with working demos. Senior leads on every sprint review. Code reviewed, accessibility checked.
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Phase 03 · 2–6 weeks
Cutover and stabilization.
Parallel run with rollback path. On-call coverage during the launch window. Stabilization continues until incident rate trends to zero.
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Phase 04 · ongoing
Operate and evolve.
Multi-year retainer with the same team that built the product. Monthly check-ins, quarterly business reviews.
Read the full engagement model on the How We Work page.
Industries we serve
Production AI across 15 verticals.
Six core verticals where OST has the deepest engagement experience. Plus nine adjacent industries served on selective engagements.
01
Education
K-12 charter networks, higher education, public sector portals.
02
Nonprofit & Civic
Donor-cycle nonprofits, advocacy organizations, civic platforms.
03
Healthcare & Wellness
HIPAA-aware platforms, medical directories, telemedicine adjacency.
04
Real Estate Technology
Multi-tenant SaaS, brokerage tools, self-storage operators.
05
E-commerce & Marketplaces
OpenCart specialists, custom commerce, $10B+ in transactions processed.
06
Manufacturing & B2B
Industrial platforms, B2B safety-tech, embedded engineering teams.
Also serves on selective engagements
Frequently asked questions
Common questions on AI prototype-to-production engagements.
What makes a prototype not production-ready?
Typically: no retry logic on API failures, no cost monitoring, no latency budgets, no structured output validation, and no observability. Prototypes are optimized for the happy path. Production requires handling every failure mode.
What does this engagement cost?
Prototype audit plus hardening of a single feature starts around $30K. Full production build with RAG pipeline, monitoring, and MLOps runs $75K to $175K. See our AI ROI calculator for a defensible bracket.
Can you work with our existing prototype code?
Yes. We start with a code review and audit of the existing prototype before recommending what to keep, refactor, or replace. Most prototype code has good bones that just need hardening around it.
How do you handle AI cost monitoring?
Token-cost tracking per query, hard ceilings, fallback to cheaper models when traffic spikes, and graceful degradation when limits hit. Cost should be predictable month to month, not a surprise.
What about hallucinations and accuracy?
RAG grounding constrains the model to your documented content. Refusal paths for out-of-scope questions. Continuous evaluation with synthetic test sets and real user feedback loops.
Ready to ship?
Pick a path forward.
Multiple ways to start: schedule a discovery call, run our cost calculator for a budget bracket, or use the contact form for a written response.