AI implementation · San Francisco
Not just in demos. We design, build and operate AI systems shaped around your workflows, your data and your business logic.
Built on your stack. Measured in cost per request, not slides.
The problem
The prototype answered every question in the meeting. In production it hits data it has never seen, tools it was never wired into, and a monthly bill nobody modelled. Off‑the‑shelf tools and generic LLM wrappers stop at the edge of your workflow, which is exactly where the useful work starts.
What we build
Representative projects. Every engagement is shaped around one client's workflow, data and rules, so no two builds are the same.
Support & sales 01 Your queue is mostly the same twenty questions. Your team should be on the rest.
Operations 02 Invoices, contracts and forms, read and re-keyed by hand. Every single day.
Research & content 03 Analysts spend the week gathering. The thinking gets squeezed into Friday.
Support & sales 04 Good leads wait in the same inbox as the noise.
Operations 05 The answer exists somewhere in the wiki. Nobody can find it.
Research & content 06 Thousands of product pages, emails and posts. One small team.
Operations 07 Exceptions, updates and follow-ups that fall between your systems.
Bring the process your team dreads. We'll tell you on the first call whether AI is the right tool for it, and what it would take to run in production.
Book a callHow we deliver
Most agencies hand over a prototype. We design it, build it, wire it in, and keep it running as your load and requirements change.
Purpose-built systems shaped around your workflow, not a generic chatbot with your logo on it.
Calibrated against your real cases until it is accurate enough to trust in front of customers.
AI that reads and writes where the work actually happens, inside the tools your team already uses.
The unglamorous part that decides whether it survives month three.
Model-agnostic by design. We choose current frontier models, open-weight models or a mix of both per task, on accuracy, latency and cost, and we can swap them later without rebuilding your system.
Integration
An AI that can't touch your systems is a chatbot. Yours works where the work is.
It serves your system only and never trains anything shared with anyone else.
What it may do alone, what needs a human, and what it must never touch.
What it read, what it decided, and why. Auditable after the fact.
Models and providers are swappable parts, not a rewrite.
How we work
We map the workflow, the data and the cost of getting it wrong, and tell you plainly if AI is the wrong tool for it.
You get: a scoped plan and a cost modelA purpose-built system, evaluated against your real cases rather than the ones that demo well.
You get: a working system and its test setWired into your tools with the guardrails you set. Live on one workflow first, wider once the logs convince you.
You get: it running on real trafficDeployed, monitored and tuned as load and requirements change. We stay on after launch.
You get: weekly accuracy and cost reportsThe question you didn't ask
Fair. Most AI pilots never find out. This is what goes in before launch, not after the first incident.
| System | Req / day | p95 | Cost per 1k requests |
|---|---|---|---|
| Support assistant | 12,400 | 1.9s | $3.10 |
| Document extraction | 3,800 | 4.2s | $5.40 |
| Research pipeline | 640 | 38s | $10.40 |
| Lead triage | 2,100 | 1.1s | $1.20 |
SignalResearch pipeline spend up 18% week on week as source volume grew.
ActionCache source fetches, move summaries to a smaller model. Same output quality, re-measured before rollout.
Why Agentuned
Most AI projects work in demos and fail in production. Ours don't, because we operated AI systems handling live customer workloads before starting this company. We know the infrastructure, the cost-per-request maths, the failure modes, and what it takes to keep a system reliable as load grows.
Multi-step, multi-model systems that route each task to the right model and recover when one fails.
Tracing and evaluation across many customers at once, so a regression shows up before a complaint does.
Deployments sized to the traffic you have, with headroom planned for the traffic you expect.
What we commit to
The way we work, stated plainly. Ask us about any of them on the call.
Questions
Buy one if it fits. It is cheaper to start and running this afternoon. The difference shows up at the edge of your workflow: your data, your rules, your systems. When a generic tool stops there, you need something built around how you actually work.
Whatever fits the job. Current frontier models, open-weight models where cost or privacy calls for them, and the agent and automation tooling mature enough to run in production. We are not tied to one vendor, so you are not either.
We start with one workflow and get it live on real traffic before widening scope. If you are months in and still looking at slides, something has gone wrong.
It serves your system only and never trains anything shared with anyone else. Access to your tools is scoped to what the system needs and revocable by you at any time.
No. Operating the system is part of the work: monitoring, cost control and tuning as your load and requirements change.
Who we build for
Find yourself below. That's where we'd start you, and we'd tell you on the call if we thought you should start somewhere else.
Sources in, structured findings and on-brand drafts out, with every claim traceable back to where it came from.
Conversations with real workflow depth: it looks up the account, follows your policy, and knows when to hand over.
The repetitive reading and re-keying your team does today, done accurately and checked before it lands in your systems.
Book a call
You'll talk to the engineers who would build it, and leave knowing whether AI is the right tool and what it would take to run in production.
