AI that earns its keep.
Most AI demos collapse the second they touch your data, your tools, or a real customer. We build agents, automations, and retrieval systems that hold up under load — with evals, traces, guardrails, and a kill switch you can actually find.
Concept · SimulatedA staged run showing the instrumentation every build ships with.
- Agents · LangGraph · custom
- RAG · pgvector · Pinecone
- Evals · traces · guardrails
- OpenAI · Anthropic · Bedrock
- Voice · Realtime API
- LangSmith · Langfuse
- Slack · Linear · Zendesk
- Postgres · Snowflake · BigQuery
Six practices, one system.
We don't sell "AI." We sell working software that happens to use models — wired into your stack, instrumented end-to-end, and owned by an engineer on our team until you say otherwise.
Agents that take action
Tool-using agents with a planner, a guard, and human-in-the-loop checkpoints. They open tickets, draft emails, run reports, and stop when confidence drops.
RAG with cite-back
Retrieval pipelines that ground answers in your docs — every claim links to a chunk, every chunk links to a source. Hybrid BM25 + vector, re-ranked, deduped.
Workflow automations
Event-driven workflows that hand off between models, services, and people. Webhooks in, structured output out — versioned, replayable, observable.
LLM apps & copilots
Custom interfaces for the work your team actually does — drafting, summarizing, classifying, comparing — with model routing, streaming, and a UI that matches your product.
Voice, vision & multimodal
Realtime voice agents, document understanding, image classification, OCR pipelines. Live transcripts, barge-in, function calling — without the WebRTC headache.
Evals & observability
You cannot improve what you cannot measure. We instrument every run with traces, golden sets, regression evals, and dashboards your eng + PM team will actually open.
Every run, traceable.
What happens between "user asks" and "answer ships" is the entire job. We expose it: the graph, the tool calls, the latency, the tokens, the evals. If something regresses, you'll know before the customer does. The run below is a simulation — it shows the instrumentation every build ships with.
Plays nicely with your stack.
We meet your data and tools where they live. No "rip-and-replace" required — pick the model, the vector store, the orchestrator, and the observability layer that fit your constraints.
Model providers
Frameworks & runtimes
Vector & search
Evals & traces
Tools we wire agents into
Where the data lives
Three builds we're built for.
Illustrative scopes — sized, staffed, and ready to deliver. Use them as a starting point for your own brief; we adapt scope, stack, and timeline to your reality.
These are representative engagement scopes — not customer case studies. Budgets, timelines, and stacks reflect what we propose for builds in this category. Our own proof: we run LLM-powered SEO analysis in production inside our SaaS platform, on the Anthropic API — we operate this stack daily, on our own product.
Support deflection agent
A Slack + Zendesk agent that triages incoming tickets, drafts a grounded reply with citations from your docs, executes safe actions (refund within policy, tag, escalate), and hands off to a human the moment confidence drops or the customer asks for one.
Sales-research copilot
A copilot for AEs and SDRs: pulls account history from CRM, web-research signals, and product-usage data, then drafts the brief, the email, and the next-best-action — with a one-click "send to HubSpot" tool call. Built for daily use, not the demo.
Document understanding pipeline
An ingestion pipeline for contracts, invoices, or research papers: OCR + layout, structured extraction with schemas you control, validation, human review queue, and downstream API hand-off. Reproducible, replayable, audit-friendly.
The full seven-layer stack.
Most teams underestimate how many layers a real AI feature needs. Skip any of these and you're shipping a demo. We default to all seven — and tell you which can be deferred for your scope.
Six things we won't ship.
We've watched enough AI demos die at the production line. The discipline is in what you refuse to do — these are ours, written down, and applied to every engagement.
From audit to operating.
Most "AI projects" stall between prototype and production. We bake the harden phase into the engagement so the path to a real launch is the default path.
Audit & shape
We meet your team, your data, and your constraints. Pick the use-case with the highest signal-to-effort. Write the brief together.
Working slice
A real, end-to-end vertical slice — your data, your tools, your model — running in your environment within two weeks. Not a notebook. A runnable build.
Eval, guard, ship
Golden sets, regression CI, guardrails, traces, dashboards. Pen-test the agent. Wire into your auth, your RBAC, your audit log. Ship to production.
Retain & evolve
A monthly retainer with on-call eval review, model upgrades, prompt iteration, and feature work. We stay attached as long as you want us.
We don't disappear after go-live.
Most agencies ship and vanish. We built three simple retainers so you can keep us around at the level that matches your team — from a light monthly cadence to embedded leadership.
Launch, runbook, training — and we're off. Great if your team can own it from here.
- Production launch + DNS
- CMS training sessions
- Runbook + Loom library
- 30-day bugfix warranty
Ongoing partnership for teams that want us in the room as they grow.
- Monthly page/feature drops
- Performance + CWV monitoring
- Quarterly design reviews
- Priority Slack channel
- SLA: 1-business-day response
We plug into your team as fractional design + engineering leadership.
- Dedicated senior designer + engineer
- Weekly planning + demo
- Roadmap co-ownership
- Hiring + interview support
- Full Figma + repo access
Twelve sharp questions.
The questions thoughtful CTOs and operators ask before signing. Direct answers, no marketing edges sanded off.
Whose models do you use?
Whichever fit. We deploy on OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Vertex, and self-hosted open weights — picked per task and per data-residency requirement, not per fashion. In our own SaaS platform, the Anthropic API powers LLM-based SEO analysis in production today.
Where does our data go?
Wherever you tell it to. We default to providers with zero-retention enterprise terms and VPC-private deployments (Bedrock, Azure OpenAI). For regulated or sovereign workloads we ship self-hosted open-weight models — no egress, full audit log, your KMS keys.
How do you prevent hallucinations?
Hallucinations are reduced by architecture, not by prompting. We ground answers with retrieval, force structured output where appropriate, run policy-eval guardrails, and gate every release on a golden set with grounding scores. We do not promise zero hallucination — anyone who does is selling something.
How do you measure agent quality?
Per-feature golden sets (typically 100–300 cases) scored on tool-choice correctness, response grounding, refusal appropriateness, latency p95, and cost p95. Evals run on every PR; merge fails if any gate regresses. Production traces are sampled into the eval queue weekly.
Do you do voice agents?
Yes. Realtime voice with OpenAI's Realtime API or Vapi/Retell for telephony. Live transcripts, barge-in, function calling, hand-off to human. Production-grade voice carries extra latency and reliability work beyond a prototype, and we will scope that honestly up front.
Can you fine-tune a model?
Yes — but rarely the first move. For most tasks RAG, prompt engineering, and output schemas outperform fine-tuning at lower cost and risk. When fine-tuning is right (style, domain language, tool-use distillation) we do it on OpenAI, Anthropic, Bedrock, or open weights with LoRA.
How do you handle prompt injection?
Defense in depth: input classification, instruction hierarchy, output schemas that don't allow the model to issue arbitrary tool calls, allowlists per tool, scope-bound credentials per agent run, and anomaly detection in traces. We assume malicious inputs and design accordingly.
What does an engagement cost?
Most production AI builds we scope land between $70K and $250K, with monthly retainers from $6K for ongoing eval review and iteration. We will give you a fixed-price discovery (~$8–12K) that produces a written brief, an architecture, and a build estimate within ~10 business days.
How long until something runs?
A working vertical slice on your data inside two weeks of kickoff is our standing target. Production-hardening (evals, guardrails, observability, RBAC, audit) typically adds 4–8 weeks depending on integrations and security review.
Do you sign a BAA / DPA?
Yes. We sign DPAs as standard. We can sign a BAA for HIPAA-adjacent work, with the model layer on a HIPAA-eligible provider (Bedrock, Azure OpenAI under BAA). For HIPAA work the architecture changes — we will scope it explicitly.
Who owns the prompts, evals, and code?
You do. Standard MSA assigns all work product to you on payment, including prompts, eval datasets, retrieval pipelines, and source code. We retain rights only to general-purpose tooling we wrote before the engagement.
Will this still work in 12 months?
Yes — and we'll be the ones keeping it working. Models change every quarter; the harness around them shouldn't. We pin model snapshots, version prompts, run regression evals on every model upgrade, and roll forward only when the eval gates pass.
Tell us what you're building.
Six quick steps, two minutes. A senior team member reads every brief — not a form auto-router — and replies within one business day.
Tell us about your company.
The basics — so we walk into the call already halfway up to speed.