Working agents, not prompts
Full agent architecture — system prompt, tools, memory, skills. Production-ready from day one.
We build AI agents that actually work.
Tell us the problem. We build the solution.
Most businesses know AI could help them. Few know how to build it properly. We do.
Built with
ClaudeMCPCursorClaude CodeKiroOpenClawServices
What we build
Full detail →Operational agents
Automate the repetitive internal tasks eating your team's time. Document routing, data extraction, report generation, classification.
Product and analysis agents
Research, competitive analysis, market monitoring, data interpretation. The analytical heavy lifting so your team focuses on decisions, not data gathering.
Workflow and process agents
We map your process first, find where the effort actually goes, then build the agent that removes the manual work. Often the most valuable thing we do is the mapping before we build anything.
Why us
Process first. Then technology.
Clear delivery
You'll know what we're building, when we'll deliver it, and what it costs — before we start.
Full structure
MCP integrations, tool configuration, edge-case testing. Everything between prompt and production.
Read our story →We start with your business, not the technology. Ten years of process mapping means we usually find the real opportunity before you've finished describing the problem.
Built on experience. Not experiments.
- 20+
- years product leadership
- 5
- founding client builds
- 30
- day support on every build
Work
Recent builds
See all work →Product
Competitive research agent
SaaS startup, 15-person team
2 roadmap calls in month one
Problem
The product team needed weekly competitor intelligence but had no resource dedicated to it. Research was ad-hoc, inconsistent, and often weeks out of date when it reached the team.
What we built
Built a research agent that monitors competitor sites, product pages, pricing, job postings and review sites on a weekly cycle. Synthesises changes into a structured brief with commentary on what changed, why it might matter, and what warrants a closer look.
Result
Consistent weekly competitive intelligence delivered without manual effort. Product team made two roadmap decisions based on signals surfaced by the agent in the first month.
Claudeweb search MCPstructured output
Operational
Document intake and classification agent
Financial services, 22-person team
97% routing accuracy
Problem
The team received 50-80 documents per day across email and a shared drive. Staff spent 2 hours daily reading, classifying and routing each one manually. Backlogs built up. Documents occasionally went to the wrong team.
What we built
Built an agent that monitors the intake channels, reads each document, classifies by type and urgency, extracts key fields, and routes to the correct team queue with a structured summary. Edge cases flagged for human review.
Result
2 hours of daily manual work automated. Routing accuracy 97%. Human review queue reduced to an average of 6 items per day.
ClaudeMCPdocument processing