AI & Automation
AI features that solve one real problem. Well.
Most "AI projects" are demos that never leave a slide. We build small, focused AI features that ship to production: a RAG-backed search over your docs, an agent that drafts your proposals, an automation that eats your Friday inbox. Narrow scope, measurable outcome.
What's included
RAG over your knowledge
A search-and-answer system built on your own docs, drive or database. Grounded answers with citations, latency under a second, and an eval harness so quality doesn't drift.
Agent for a bounded task
One agent, one task, one success metric. Drafts a proposal, triages support email, researches a prospect, schedules a meeting — and hands off clean to a human when it's not sure.
Workflow automation
Zapier / Make / n8n or a custom Node service. Connect the tools you already use (Slack, Notion, HubSpot, Sheets, GitHub) and reclaim the hours that used to go to copy-paste.
Internal tool / dashboard
A lightweight internal app that exposes your data + your AI features to the team. Auth, roles, an audit log — the boring stuff that makes AI safe to use at work.
Model routing & cost control
Multi-model architecture (Claude, GPT, local) with fallbacks, caching, cost tracking and rate limiting. You ship the right model for each task, not "the fanciest one".
AI strategy sprint
A 2-week engagement: map the high-value AI opportunities in your business, rank by ROI / effort, and leave with one ready-to-build spec + three ruled-out.
How it runs
- 01
Narrow the scope
A one-page spec: the task, the user, the success metric. Most AI projects fail at the brief — we over-invest here on purpose.
- 02
Prototype fast
A working prototype in 1–2 weeks, with real data and 10 test cases. We decide go / no-go before building infrastructure.
- 03
Build to production
Proper logging, cost tracking, evals, guardrails and fallbacks. Deploy behind a feature flag so you can roll back without drama.
- 04
Measure & tune
Weekly quality reviews against your metrics. Prompt iteration, retrieval tuning, and occasional model swaps. Nothing stays static — neither should the feature.
A good fit for
You drown in the same 10 questions
Support, sales, internal ops — same questions, different day. A RAG assistant trained on your docs answers the 80% of common questions, escalates the rest.
You have proposals / reports to write
Each proposal takes 4 hours of cut-paste from previous ones. An agent with the brief + your past work drafts a 70%-there version in 5 minutes. You finish.
You run a process across 5 tools
Lead comes in → check CRM → enrich → draft email → log in Notion. An automation does it silently, you review the exceptions.
You want to start somewhere safely
A strategy sprint to find the single highest-ROI AI project in your business and ship just that. No "AI transformation" theatre.
Deliverables
Frequently asked
What models do you use?
Primarily Claude (for quality + long context) and GPT (for breadth). Local models (Llama, Mistral) when data can't leave. We route per task, not per fashion.
Where does our data live?
Your cloud, your database. We deploy on Vercel, AWS, or your on-prem infra. Enterprise plans use zero-retention endpoints. Nothing trains a public model.
How do you handle hallucinations?
Retrieval grounding with citations, structured outputs with schemas, evals that run on every deploy, and human-in-the-loop for anything stakes-critical. We design for "I don't know" as a valid answer.
How do you price it?
Fixed price per phase (discovery, prototype, production). We share token cost estimates up front so there are no billing surprises. You own the API keys.
Ready to start?
Send a short brief and we'll reply within one business day.