Production AI — not demos, not slideware.
We build AI features that survive contact with real users and real data. RAG, agents, copilots, evals, and guardrails — engineered for the workflows your team actually uses, every day.
The gap between an AI demo and AI in production
Almost any team can spin up a ChatGPT demo over the weekend. Almost no team can get that same demo to work reliably on real customer data, with audit trails, evals, and a path to improve over time. That gap is where most AI projects die.
We build the production-grade version. Retrieval pipelines that work on your messy real data. Agents that don't hallucinate critical fields. Copilots that improve from real usage, not just from prompt tweaking. Evals that catch regressions before customers do.
Most importantly, we build AI that integrates into the software your team already uses — not yet another chat window your team has to learn.
What we build with AI
- Retrieval-Augmented Generation (RAG) over your documents, knowledge base, or operational data.
- Agentic workflows — multi-step plans with tool use, retries, and confidence scoring.
- Domain copilots embedded directly inside your existing software (support, sales, ops, clinical).
- Structured outputs and JSON schemas that downstream code can actually trust.
- Eval harnesses and golden datasets — every release gets scored before it ships.
- Guardrails — content filtering, PII redaction, prompt injection defense, response policy enforcement.
- Model selection and cost optimization — Claude, GPT, Llama, on-prem, or open-source where it fits.
- Observability — every prompt, response, latency, cost, and feedback signal logged for analysis.
- Continuous improvement loops — production traces feed back into evaluation and fine-tuning.
How we deliver
- 01
Use-case scoping
We pick the right surface for AI — automation, copilot, agent, or batch processing — and design around the cost / quality / latency tradeoffs you actually care about.
- 02
Data + retrieval foundations
Indexing, embeddings, chunking strategy, hybrid retrieval. We measure recall before we measure quality.
- 03
Evals first
Golden dataset, automated grading, a baseline score before a single LLM call goes to production. Evals are non-negotiable.
- 04
Build + integrate
We embed the AI inside your existing product — API, UI, and the workflow your team already runs.
- 05
Ship + iterate
Production launch with logging, monitoring, cost controls, and a feedback loop that improves the system without manual prompt engineering.
Industries we ship this for
Engagement & pricing
Best for: embedding AI into an existing product or workflow.
Best for: building an AI product from scratch — copilot, agent, internal AI tooling.
Best for: running an AI product post-launch with eval, observability, and continuous improvement.
Frequently asked
Which models do you use?+
We pick per use case. Claude (Anthropic) and GPT-4-class models for general work, Llama 3 / Mistral for on-prem or cost-sensitive deployments, and embedding models specific to the domain. We're not loyal to any vendor.
Can we run AI on our own infrastructure?+
Yes — we deploy open-source models (Llama, Mistral, Qwen) on customer-controlled GPUs when data residency, cost, or compliance demands it. Common in healthcare and finance engagements.
How do you handle hallucinations?+
Three layers: retrieval gives the model grounded context, structured outputs constrain what it can return, and evals + monitoring catch drift over time. Critical fields never come from the model alone — they're validated against source data.
What about data privacy and HIPAA / PIPEDA?+
We design for compliance from day one. Common patterns: PII redaction before the LLM call, on-prem or zero-data-retention API agreements, audit logging of every prompt and response, and consent-aware data flows.
Will AI replace our engineers?+
Not the kind of AI we build. Production AI augments humans — surfacing context, drafting responses, automating routine decisions — while leaving the high-stakes calls (and the accountability) with people.
Do you do fine-tuning?+
When it makes sense. Most production AI needs better retrieval and better evals long before it needs fine-tuning. When fine-tuning is the right call, we build the dataset and ship the fine-tune as part of the engagement.
Talk to us
Tell us what you're trying to ship and what's in the way. You'll get a real reply from a senior engineer — not a sales script.