Solutions
Built around the problems that matter
Most engagements start with one of these problems. Each one maps to the engineering disciplines that solve it — and often more than one.
Six business problems we solve
AI prototypes that never reach production
- Our approach
- Embed engineers with the team, add the integrations, evaluation, access control and monitoring the prototype is missing, and release it in stages.
- What improves
- An AI feature people rely on every day, with known quality.
Repetitive workflows spread across disconnected systems
- Our approach
- Map the workflow, connect the systems through governed tools, and let agents handle routine steps with approvals where they matter.
- What improves
- Less manual re-keying and faster turnaround on routine work.
Enterprise data that is hard to search and use
- Our approach
- Retrieval over approved sources with permission filtering, answers that cite their sources, and quality measured against real questions.
- What improves
- Answers people can find and verify in seconds rather than hours.
Cloud infrastructure that is expensive or unreliable
- Our approach
- Assess each workload, define it in Terraform, add observability and service-level objectives, and put cost ownership in place.
- What improves
- Predictable spend and fewer surprises in production.
Delivery pipelines that slow down releases
- Our approach
- Automated build, test and security gates with GitOps deployments, progressive rollouts and automatic rollback.
- What improves
- Smaller, more frequent and safer releases.
Legacy applications that limit growth
- Our approach
- Modernize incrementally — new modules and APIs alongside the old system — so the business keeps running throughout.
- What improves
- A codebase that can take on new features and integrations.
Don't see your problem here?
Tell us what you're trying to fix — we'll tell you honestly whether we can help.