Production AI agents and GenAI systems
We build applied LLM and agentic systems that run reliably in production — grounded in your data, measured by evals, and shipped with the guardrails and observability real workloads demand.
▸ Most teams can build a prototype. We build the production layer.
Why most agent and LLM pilots never ship
A demo that works on a clean example is not a system that holds up under real traffic. The hard part is everything that happens after the prototype.
Source: Menlo Ventures, State of Generative AI in the Enterprise 2025
No evals
Without an automated way to measure quality, every change is a guess. Teams cannot tell whether a tweak helped or quietly broke something, so they stall before going live.
Unmanaged hallucination
Outputs that look confident but are wrong. With no grounding, structured outputs, or verification, the system cannot be trusted with anything that matters.
Cost and latency surprises
Token spend and response times that are fine in a demo become unworkable at scale. Without budgeting, routing, and caching, the economics fall apart.
Brittle prompts
Hand-tuned prompts that work today and fail on the next model update or edge case. Prompt-only systems are fragile and hard to maintain.
No observability
When something goes wrong in production, there is no trace, no logging, and no way to reproduce it. Debugging becomes guesswork.
No real integration
A model that cannot reliably call your tools, read your data, or fit your existing systems stays a sandbox toy. Production needs dependable integration.
Applied GenAI, engineered for production
A focused set of systems we ship end to end — grounded in your data and your workflows, not generic demos.
Agentic workflow automation
Multi-step agents that plan, take actions, and complete real work across your tools — with explicit control over when they act autonomously and when a human steps in.
RAG over your proprietary data
Retrieval-augmented generation grounded in your documents, records, and knowledge bases — so answers cite real sources instead of inventing them.
LLM integration into existing products
Generative features embedded directly into the products and internal tools you already run, designed to fit your stack, auth, and data boundaries.
Eval & guardrail harnesses
Automated evaluation suites that quantify quality, plus guardrails for safety, format, and policy — so you can change the system with confidence instead of guesswork.
Human-in-the-loop orchestration
Review, approval, and escalation flows that keep people in control of high-stakes decisions while the system handles the routine volume.
Tool / function calling & integration
Reliable function-calling and system integration so the model can query your APIs, trigger actions, and operate inside your existing infrastructure.
Engineered to reach production, not just a demo
The difference between a pilot and a shipped system is discipline. Here is how we close that gap.
Eval-first
We define how success is measured before we build, then keep an automated eval harness running so quality is provable and regressions are caught early.
Observability built in
Tracing, logging, and dashboards for quality, cost, and latency from day one — so you can see how the system behaves in production and act on it.
Iterative shipping
Short iterations with working software you can evaluate, instead of a long black-box build. You see progress and steer direction throughout.
You own the IP
All code, prompts, eval suites, and infrastructure are yours. No black boxes, no lock-in — everything transfers cleanly when you want it to.
Engagements start at $100K
Priced on the outcomes we deliver, not the hours we bill. We take on focused, production-grade builds where a working system in your hands is the point — not a slide deck.
You work directly with senior engineers — no handoffs to a junior bench.
We scope around the result you need in production, then deliver against it.
Code, prompts, and IP are yours. NDA on request, security-conscious engineering throughout.
Frequently asked questions
Straight answers about how we build, what it costs, and what you walk away owning.
Off-the-shelf copilots are built for the average company, not yours. They do not know your data, your workflows, or your edge cases, and you cannot change how they reason or where they break.
We build systems on top of your proprietary data and processes, with evals that prove the behavior you actually need and guardrails you control. You own the code and can keep evolving it instead of waiting for a vendor's roadmap.
We treat these as engineering problems with measurable targets, not as things you hope improve.
Hallucination is reduced through grounding (retrieval over verified sources), structured outputs, and an eval harness that catches regressions before release.
Cost and latency are managed with model routing, caching, prompt and context budgeting, and choosing the smallest model that still passes the evals.
We instrument the system with observability so you can see token spend, latency, and quality over time rather than guessing.
Yes. You own all of the code, prompts, eval suites, and infrastructure configuration we build for you. There is no proprietary black box and no lock-in to us.
If you later want to bring the work in-house or hand it to another team, everything transfers cleanly with documentation.
Timelines depend on scope, data readiness, and integration surface, so we scope each engagement individually.
In general we work in short iterations: an early version backed by evals, then hardening for reliability, cost, and observability before it carries real traffic. We ship incrementally, so you see working software early rather than waiting months for a single release.
It depends on the use case, but typically we need representative examples of the documents, records, or interactions the system will work with, plus access to the systems it must integrate with.
During discovery we assess data quality and readiness honestly and tell you if something needs cleanup before it can be used. We sign an NDA on request and work within your security requirements.
Have an AI system that needs to reach production?
Book a call to talk through your use case. We will tell you honestly what it takes to ship it reliably — and whether we are the right team to build it.
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