AI where it earns its keep, and nowhere else.
Practical AI integration that solves a real bottleneck — not AI for AI's sake. We help you identify where machine learning earns its keep, build it cleanly, and avoid the failure modes that have made "AI rollout" a synonym for "vendor lock-in" for many organizations.
We start with the question "what would a 30% improvement on this specific workflow be worth?" and work backward. If the answer is "a tool already exists that's good enough," we say so. If the answer is "build a custom model," we scope it like any other software engagement: written requirements, milestones, acceptance criteria.
- ✓Custom ML models for narrow, well-bounded problems (classification, extraction, ranking)
- ✓Document intake automation — OCR + structured extraction + human-in-the-loop review
- ✓Natural language search over your own corpus (without sending it to a third party)
- ✓Predictive analytics with auditable feature engineering, not opaque black boxes
- ✓AI strategy review — which of the 47 vendor pitches you've heard are actually worth a pilot
- ✓Privacy-preserving deployment patterns (on-prem inference, federated approaches, content-free pipelines)
Organizations with a specific high-volume manual workflow they suspect could be partially automated, or a strategy team that needs a reality check on a pending vendor decision.
A small-claims litigation practice was spending 6 hours per case extracting deadlines from court filings. We built a document-intake pipeline that flags every deadline mentioned in incoming PDFs, attorney reviews and confirms in 5 minutes per case. Throughput up 4x.
Fixed scope, fixed price, agreed in writing — see how we work.