Build yours
We build the product your business does not have.
The first line below is the main event: an AI-native vertical SaaS product, owned by you, built on the same spine our own two products run on. The other three exist separately because a business that already has a product and needs one piece of this should be able to find it. The timelines are the range we actually see, not the range that wins a pitch.
01
Typically 8 to 16 weeks
Vertical SaaS builds
The problem
The software your industry runs on was written for a market, not for your work. You have configured it as far as it goes, and the gap between it and the job is now held together by exports, a spreadsheet, and someone's memory.
How we approach it
We build the product that closes the gap — the whole thing, not an integration on top of somebody else's. Interface, application, permissions, billing, and the AI where it earns its place. It starts from the spine and the scaffolding we already run, which is the only reason this is a sensible thing to commission rather than a two-year programme.
What you receive
A multi-tenant application in your cloud, with authentication, billing, an admin surface, the evaluation suite, and the deployment pipeline that ships it.
A concrete example
A logistics platform where dispatchers plan routes, carriers accept jobs on mobile, and finance reconciles the whole thing without a spreadsheet in the middle.
02
Typically 4 to 10 weeks
AI in production
The problem
An agent works in a notebook and fails in the world. No retry path, no permissions model, no record of what it did, and no way to tell whether last week's prompt change broke it.
How we approach it
We build the agent as a production service: scoped tool access, explicit escalation, tracing on every run, and an evaluation suite that gates every change. This is the part of an AI-native product that decides whether it survives contact with real users.
What you receive
A deployed agent with tool permissions, retry and escalation behaviour, per-run traces, and an eval suite wired into your CI.
A concrete example
A support agent that reads the ticket, pulls order history, drafts the reply, and hands anything touching a refund to a person with its reasoning attached.
03
Typically 3 to 8 weeks
Platform and cloud
The problem
Deploys are a person, not a pipeline. Nobody is sure what is running in production, and the one who set it up left.
How we approach it
We put the platform on rails: infrastructure as code, environments that match, observability that answers questions, and a release process anyone on the team can run.
What you receive
Reproducible environments, a deployment pipeline with rollback, dashboards and alerts that map to real failures, and the runbook to go with them.
A concrete example
Moving a hand-configured cluster to declarative infrastructure, with preview environments per pull request and a rollback that takes one command.
04
Typically 2 to 6 weeks
Data and integration
The problem
The model is not the hard part. Getting clean, current, permissioned data out of the systems of record is the hard part, and it is where most projects quietly stop.
How we approach it
We do the connector and schema work properly: incremental sync, access control that matches the roles you already have, and a data layer the rest of the product can rely on.
What you receive
Connectors to your systems of record, the schema and access work that makes them queryable, and tests that catch it when an upstream field changes.
A concrete example
Reconciling customer records across a legacy CRM, a billing system, and a warehouse so one query answers what currently takes three tabs.
Next
Describe the process and we will tell you which of these it is.
If the honest answer is that an off-the-shelf product would cover it, we will say that too, and tell you which one. Turning down a build we do not think is worth doing costs us less than finishing one.
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