Off-the-shelf AI tools are built for the average use case. Your business isn't average. Your data is structured differently, your review process has exceptions, and your team has learned workarounds that matter.
We build AI workflows around those realities - not against them. The result is automation that actually fits how your team works, not a tool that forces your team to adapt to it.
What we build
- Document intake and processing - extract structured data from unstructured documents, route to the right queue, flag exceptions for review
- Support triage and response drafting - classify incoming requests, pull relevant context, and draft responses that a human reviews before sending
- CRM enrichment and outbound personalization - keep records accurate and generate outreach that reads like it was written by someone who did their homework
- Data cleanup and reconciliation pipelines - identify inconsistencies, merge duplicates, and flag gaps across systems that don't talk to each other
How we work
1. Scoping. We start with one workflow, not a platform. We map the current process, identify where AI adds value, and define what "done" looks like before we write a line of code.
2. Build. We build the workflow with observability and guardrails from the start - not bolted on after. You can see what the model is doing and why at every step.
3. Deploy and iterate. We ship to production with you, monitor the first real runs together, and tune based on what we see. Most workflows are stable within a few weeks of live use.
Our principles
- One production workflow beats a roadmap full of unfinished ones
- Humans stay in the loop wherever the cost of a mistake is high
- Observability isn't optional - you should always be able to audit what the model did
- We don't disappear after launch
Good fit if
- You have a manual process that's slow, error-prone, or eating up time your team can't spare
- You've tried to use a generic tool and kept running into edge cases it couldn't handle
- You need something that integrates with your existing systems rather than replacing them