How do you keep LLM spend predictable?

Three levers: (1) model routing: cheap models for cheap tasks, (2) prompt caching for high repetition prefixes, (3) response streaming with early cancellation. On typical support/copilot workloads we cut spend 40 to 70% versus a naive 'always call GPT 4' implementation.

Can you build agentic systems that call our internal tools?

Yes. We build agents with function calling (OpenAI/Anthropic native) or open source frameworks (LangGraph, Vercel AI SDK). Every tool call is bounded by policy (the agent can only call what we've explicitly registered) and every action is logged with a full reasoning trail.

How do you handle prompt versioning and rollback?

Prompts live in code, not admin panels. Every change is a PR reviewed like any other. Deployments go through the eval harness; regressions block merge. Rollback is a git revert: no dashboard hunting.

What's a realistic first engagement?

Four to eight weeks for a first production LLM feature: enough for us to build the eval harness, ship one high value use case, and hand your team the pattern. Team augmentation continues from there if you want us in house.

AI & Machine Learning

Generative AI Development

From prototype to production: LLM engineering that survives real users, real regulators, and real budgets.

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What is generative AI development?

The Answer

Generative AI development is enterprise LLM engineering — prompt tuning, agentic systems, and production grade GenAI applications built on OpenAI, Anthropic, or open weight models. The focus is on reliability: eval driven development, cost optimization through model routing, and production observability so quality issues get caught before users see them.

What you get

Three outcomes we commit to before we start.

Eval driven from day one

We write the golden test set before the first prompt. Every model change, prompt tweak, or retriever update is scored against it. No 'looks good' shipping decisions.

Cost aware architecture

We route by model tier: Haiku for classification, Sonnet for reasoning, Opus for edge cases. Prompt caching, retrieval trimming, and streaming reduce token spend by 40 to 70% versus naive implementations.

Production observability

Every LLM call is traced (input, output, latency, cost, quality score) with Langfuse or Datadog. Regressions are caught before your users notice.

The Guaranteed Production Pilot

Fixed scope · Written target

A working Generative AI feature shipped to production in weeks: no ML hire required.

Production grade Generative AI your users actually touch: real code, real evals, benchmarks your next round can point to. Not a prototype you have to rebuild.

Speed

First production milestone in 5 days. Full pilot live in 6 weeks, fixed scope, weekly demos.

Zero effort

Done for you end to end: you get the production code, the eval harness, and 100% IP ownership. No lock in, no data hire.

Risk reversal

We commit the first milestone in writing. Miss that first week milestone and the first week is free.

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