Customer support
Resolve routine requests, route the rest, and hand over to a person with full context.
AI agents
Agents that do real work
We build AI agents that work inside your tools and data, along with the evals, guardrails, and monitoring that keep them reliable after launch.
Use cases
Where agents earn their keep.
Typical starting points. Most projects begin with one workflow and grow from there.
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Sales and operations
Research accounts, update the CRM, and prepare follow-ups without manual copy and paste.
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Internal knowledge
Answer questions across docs, tickets, and wikis, with sources your team can check.
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Workflow automation
Run multi-step processes across your systems, with approvals where they matter.
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Data and reporting
Pull data from many sources and turn it into reports people can trust.
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Developer tools
Triage issues, review changes, and keep documentation current.
Anatomy of a run
What happens between a request and a result.
Six stages, and what we build at each one. The model is only one of them.
1Trigger
A run starts from an event, a message, or a schedule.
We connect the triggers, queue the work, and make sure a repeated event is handled once.
What we build
- Webhooks, schedules, and chat entry points
- Queues with retries and de-duplication
- An idempotency key on every run
2Context
The agent gathers what it needs, and only what it is allowed to see.
Retrieval over your documents, tickets, and databases, filtered by the requester's access before anything is searched.
What we build
- Retrieval with source citations
- Access rules enforced before search
- A context budget, so long inputs don't drown the task
3Plan
The model chooses the next step, inside a loop we control.
Prompts, routing, and limits that keep the agent on task and on budget, with fixed paths wherever the path is known.
What we build
- Model choice and routing per task
- Step, time, and spend limits
- Fixed paths where the path is known
4Act
Tools do the work: narrow, typed, and safe to repeat.
Each tool has one job, a strict schema, a timeout, and clear errors the agent can recover from.
What we build
- A small set of sharp tools
- Typed inputs, timeouts, and retries
- Dry runs for anything that changes data
5Check
Guardrails and people sit between a decision and its consequence.
Policies decide what runs alone, what needs approval, and what is never allowed. Evals gate every release.
What we build
- Allow, ask, or block, per tool
- Approvals with the evidence attached
- Evals that gate every release
6Record
Every step is traced, costed, and replayable.
So you can see what happened, find out why, and turn every failure into a test.
What we build
- End-to-end traces and audit logs
- Cost and quality dashboards
- A feedback loop into the eval suite
Patterns
A small set of patterns, combined with care.
Most agents are built from a few well-understood designs. We choose the simplest one that does the job.
Router
A model picks one of a few fixed paths. The simplest way to add judgment to a process.
Retrieval assistant
Answers grounded in your documents, with citations, and access rules applied before search.
Workflow with a model step
Your code runs the process, and a model handles the fuzzy step: classify, extract, or summarize.
Approval gate
The agent prepares the action, and a person approves it. For anything where mistakes are costly.
Planner and executor
One model plans, and narrow tools carry out each step. For open-ended, multi-step tasks.
Review loop
A second check reviews the draft before it goes out. When quality matters more than speed.
Engagement
Start small, then grow.
Agent projects work best as a sequence. Each step earns the next.
Discovery
We map your workflows, choose the one with the clearest payoff, and define how success will be measured.
Best for teams deciding where agents make sense.
Pilot
We build one agent, with evals and monitoring in place, and put it in front of real users.
Best for proving value on a single workflow.
Scale and operate
We expand to more workflows and keep improving the agents with data from production.
Best for agents already in use that need to grow.
How we work
A clear path from idea to launch.
Every project follows the same five steps, with working software at the end of each one.
- 1
Discover
We learn your goals, users, and constraints, then agree on what success looks like.
You get
- Written goals and success measures
- A map of users, systems, and risks
- 2
Design
Architecture, prototypes, and a scoped plan with milestones you can hold us to.
You get
- Architecture and a prototype
- A scoped plan with milestones
- 3
Build
Short cycles with weekly demos, shared repositories, and written decisions.
You get
- Working software every week
- Your repository, with tests and CI
- 4
Launch
Production deployment with monitoring, rollbacks, and a handover your team can follow.
You get
- A production deployment
- Monitoring, alerts, and a rollback plan
- 5
Operate
Ongoing support, measurement, and improvements as real usage teaches us more.
You get
- Quality and usage reporting
- A prioritized list of improvements
Technology
Model-agnostic by design.
We choose the model and tooling per task, and measure the result.
- LLM APIs
- Open-weight models
- MCP
- RAG
- Evals
- Tracing
- TypeScript
- Python
- PostgreSQL
FAQ
Questions we hear most.
Which models do you use?
We're model-agnostic. We pick a model for each task based on quality, latency, cost, and your data requirements, and we measure with evals so the choice can change as models improve.
How do you make sure agents are reliable?
Every agent ships with an evaluation suite, limits on what it can do, and tracing for every run. We test against realistic cases before launch and keep measuring after.
What happens when the model gets it wrong?
Sometimes it will. That's why risky actions wait for approval, every run is traced, and every real failure becomes a new test. The goal is to catch mistakes, contain them, and make sure they don't repeat.
Is our data safe?
We design for least-privilege access, keep secrets out of prompts and logs, and can work inside your own cloud environment. We're happy to sign an NDA before we see anything sensitive.
Can the agent run in our own cloud?
Yes. When your data requires it, we deploy inside your cloud account and keep data in your region. It's much easier to design for that from the start than to retrofit it.
What does it cost to run an agent?
We set spend limits per run and per day, trace the cost of every step, and use smaller models for steps that don't need a large one. You see cost next to quality, so the trade-offs are explicit.
Is this a chatbot?
Usually not. We build agents that do work: they read, decide, and act across your systems. A chat window can be one way to reach them, but it's rarely the whole product.
Will agents replace our team?
We build agents to take repetitive work off people's plates and hand off decisions that need judgment. You decide where humans stay in the loop.
Where do we start?
With a short discovery. We map your workflows, pick the one with the clearest payoff, and build a pilot you can test with real users. To warm up, try the agent blueprint tool.
Tell us what you're building
A few lines are enough. We'll reply by email to set up a call.