Build, ship, and run OpenAI in production
OpenAI on DoiT
Move OpenAI Platform from pilot to production across AWS, Google Cloud, and Azure. Right model, costs you can see, operations that hold up.
enterprise-grade OpenAI practice
Most AI projects stall after the demo
The model works. The bill, the latency, and the reliability do not. We run an enterprise-grade OpenAI practice built for one job: move AI from pilot to production.
We pair Anthropic depth with our FinOps and CloudOps DNA. Our Forward Deployed Engineers embed with your team and ship, instead of handing you a strategy deck and leaving.
Why DoiT
Plenty of firms can run a OpenAI workshop. Few can run the workload after it goes live. That gap is where we work.
Multicloud by default
OpenAI on Amazon Bedrock, plus direct API. We pick the path that fits your stack, not ours.
FinOps heritage
We have priced and optimized cloud spend since 2011. Tokens, GPUs, and provider trade-offs measured to the dollar.
Embedded delivery
Senior engineers join your team. They know your architecture, your context, and your constraints.
Instrumentation built in
Attribute AI spend by team, feature, and customer so you see what OpenAI actually costs.
what the partnership gives you
From Pilot to Production, faster
Right model. Real targets. Costs you can defend.
Right model for the job
One model for hard reasoning, another for balance and yet another for high-volume work.
Faster path to production
Reference architectures compress weeks into days.
Costs you control
Unit economics from day one, tuned for margin.
Reliability under load
HA patterns and rollback plans baked in before go-live.
Eval harnesses included
Quality is measured by automated evaluations, not guessed.
Governance wired in
Least-privilege access and clear data ownership across teams.
how we deliver
Forward Deployed Engineers, not a ticket queue
FDEs are senior cloud architects who embed directly with your team. They pair with your engineers to implement, test, and deploy. They own outcomes across the full OpenAI lifecycle: agent and workflow engineering, RAG and knowledge systems, evaluation harnesses, inference cost optimization, reliability, and security.
How the engagement runs
Five phases. Real code changes, not recommendations.
Kickoff
Align on use cases, SLOs, and the pain-now list. Map your environment.
Plan
Scope the build. Pick models, define evals, set cost and latency targets.
Ship
Implement alongside your engineers. Real changes in your codebase.
Automate
Wire in monitoring, guardrails, and policy controls. No babysitting.
Prove it
Measure quality, cost, and reliability against targets. Then move on.
Run OpenAI like production
Bursty demand, GPU scheduling, model drift, unpredictable costs. Our FDE team handles OpenAI ops the way we handle any critical workload.
GPU efficiency
Spot underutilized inference and training runs. Keep accelerators busy without waste.
Scaling patterns
Autoscaling strategies tuned for LLM and agent traffic.
Data pipelines
Harden ingestion and preprocessing so retrieval and training never stall.
FinOps for OpenAI
See token usage, provider costs, and trade-offs clearly.
Resilience by default
HA patterns, failure domains, and graceful degradation built in.
Production validation
Load tests, chaos drills, and rollback plans before go-live.
see what OpenAI actually costs
Token bills hide the truth
You know the total. You do not know which team, feature, or customer drove it. We change that.
We use kernel-level instrumentation to attribute AI and cloud spend at the source, with no code changes. Tie every API call to the workload behind it. Make trade-offs with real numbers.
What we build
Each engagement starts from a problem, not a feature.
Agentic workflows
OpenAI does the multi-step work your team does by hand today.
Knowledge and RAG systems
Answers grounded in your data, not the open web.
Coding and SDLC acceleration
Faster review, refactoring, and migration with Codex in the loop.
Support automation
Resolve tickets and deflect volume without losing the customer.
Document and contract analysis
Extract, summarize, and check at scale.
Custom workloads
Built around your stack, data, and SLOs, not a template.
Start building with OpenAI
We map your priorities and the fastest path to production.