What we do
Capabilities, not platitudes.
We don't sell 'AI strategy.' We sell shipped systems. Every engagement ends with code in your repo, evals in your CI, and someone on your team who can run it without us.
Custom AI agents
Multi-tool agents that close real loops — onboarding, support triage, sales follow-up, internal ops. Built on Claude, GPT, or whichever model fits the job.
RAG & knowledge systems
Production-grade retrieval over your docs, tickets, code, and data warehouses. Hybrid search, chunking that actually works, and citations users can trust.
Voice agents
Real-time voice agents that answer phones, qualify leads, and book appointments. Sub-700ms latency, your tools, your brand.
Evaluation & guardrails
The boring part that decides whether you ship. Eval harnesses, regression suites, safety filters, and dashboards your CTO will actually open.
Model fine-tuning
When prompting plateaus, we fine-tune. SFT, DPO, and distillation pipelines tuned for cost, latency, and the specific failure modes hurting you.
Workflow automation
AI-native internal tools that replace the spreadsheet-and-Slack stack. Built fast on Next.js + the Anthropic SDK, owned by your team day one.
How we work
Four phases. Eight to ten weeks. No mystery.
Most AI projects fail in the gap between demo and production. Our process is engineered to close that gap — by validating value early and refusing to ship anything that can't be measured.
01
Week 1
Discovery
We embed with your team, map the workflow we're touching, and define what 'shipped' actually means. Concrete success metrics or we don't start.
02
Weeks 2–3
Prototype
A working proof-of-concept against your real data — not a Figma flow. You decide whether the value is there before we spend another dollar.
03
Weeks 4–8
Production build
Hardening, evals, observability, and integration into your stack. We pair with your engineers so nothing we write becomes a black box.
04
Ongoing
Handoff & ops
Documentation, runbooks, and an optional fractional retainer for when models change, costs drift, or your scope expands.
Tech we ship on
Opinionated, but never religious.
We pick tools that survive contact with production. The stack below is our default — swap any layer when your situation demands it.
Models
- Anthropic Claude
- OpenAI GPT
- Gemini
- Llama / open-weights
Retrieval
- pgvector
- Pinecone
- Turbopuffer
- Hybrid (BM25 + dense)
Infra
- Vercel
- Fly.io
- Cloudflare Workers
- AWS / GCP
Voice & realtime
- Vapi
- Retell
- ElevenLabs
- LiveKit
Evals & observability
- Braintrust
- Langfuse
- OpenTelemetry
- Custom harnesses
Tooling
- Next.js
- TypeScript
- Python
- Anthropic SDK
Engagement models
One sprint, one build, or a long-term retainer.
We don't believe in indefinite consulting. Every engagement has a finish line — though most clients keep working with us after they cross it.
Sprint
A focused prototype, validated against your real data.
- Discovery + scoping workshop
- Working prototype on your data
- Evaluation against success metrics
- Recommendation report
Build
Production system, owned by your team at handoff.
- Everything in Sprint
- Production deployment & integration
- Eval harness + observability
- Pairing sessions with your engineers
- Runbooks & documentation
Retainer
Fractional AI team for ongoing operation and expansion.
- Senior AI engineer on call
- Model upgrades & cost optimization
- New use-case scoping
- On-call for incidents
- Quarterly roadmap review
FAQ
Answers to the questions we get most.
- How is this different from hiring an AI agency or a freelancer?
- Agencies tend to optimize for billable hours and produce demos that never reach production. Freelancers can ship, but rarely own the eval, observability, and handoff work that actually matters. We work in fixed-price phases with concrete acceptance criteria, and every engagement ends with your team owning the system.
- Which models and providers do you use?
- Whichever fits the job. We default to Claude for reasoning and tool use, GPT for some structured tasks, and open-weights when latency or cost makes it the right answer. We're model-agnostic by design — and we'll switch you to whatever's actually best, even mid-engagement.
- Will the work live in our stack or yours?
- Yours. Day one. Code lives in your GitHub org, infra runs on your cloud accounts, and credentials are yours from the start. We don't build moats out of access control.
- How do you handle evaluation and safety?
- Every project ships with an evaluation harness wired into your CI — including regression suites, safety filters, and red-team prompts specific to your domain. If we can't measure it, we don't ship it.
- What does the typical engagement look like?
- Most clients start with a 2-week Sprint to validate a specific use case, then move into an 8-week Build to take it to production, and roll into a monthly Retainer for ongoing operation. About 70% of Sprints convert into Builds.
- Do you work with early-stage startups, or only enterprise?
- Both. We've built systems for Series A startups and Fortune 500 banks in the same quarter. The methodology is the same — the cost and timeline scale with the surface area.
Have an AI project that needs to ship?
30-minute discovery call. No pitch deck, no nurture sequence — just an honest read on whether the project is worth doing and what it would take.