Service
AI features that actually earn their place in the product
We build AI and automation that fit inside your existing stack — honest about what the current models can do, careful about what they cost to run, and always reversible if the experiment doesn't pay off.
What we do
We help teams ship AI and automation features that pull their weight. That means being honest about what today’s models can actually do reliably, careful about what they cost to run at scale, and always leaving a manual fallback when the model misbehaves.
Our engagements are hands-on engineering — prompt design, retrieval pipelines, evaluation, cost controls, and integration with your existing stack. We don’t just wire up an API call and declare the feature done.
How we keep AI grounded
- Every AI feature ships with an evaluation suite so you can measure accuracy, not guess at it
- Cost budgets and observability on day one — no surprise bills at the end of the month
- Graceful fallbacks for every failure mode we can anticipate
- Prompt versioning and rollback so improvements don’t silently break existing behavior
- Plain-English documentation for your team on what the feature does and how to operate it
When this fits
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Adding AI features to an existing product (search, summarization, classification, chat)
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Document-processing pipelines that extract structured data from PDFs, emails, or forms
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Retrieval-augmented knowledge bases built on your company's own docs
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Business-process automations connecting CRM, email, spreadsheets, and internal tools
Tech stack
- LLM providers
- OpenAI Anthropic Mistral Google Gemini
- Frameworks & SDKs
- Vercel AI SDK LangChain LlamaIndex
- Vector stores
- Postgres pgvector Pinecone Qdrant
- Automation
- Python Node.js n8n Zapier
How we work
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Figure out if AI is the right tool
Not every manual process needs a model. We start by mapping the workflow and identifying where AI genuinely adds value versus where a regular script or rule would do the job for one-tenth the cost.
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Prototype on real data
A working prototype on a small sample of your actual data — not a demo. You see the accuracy, failure modes, and running cost before we scale the build.
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Build with guardrails
Production implementation with prompt versioning, eval suites, cost budgets, and graceful fallbacks when the model gets it wrong. We plan for both the success case and the day the API has an outage.
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Measure and iterate
Monitoring on accuracy, latency, and cost from day one. We tune prompts, swap models, or add a retrieval layer based on what the real traffic shows — not a hypothesis.