Labs
Labs turns repeated engineering knowledge into reusable technology.
Every system we build teaches us something about evaluation, orchestration, documents or data. Labs is where those lessons become accelerators, experiments and, when they earn it, products. It is also where we test what we are not yet ready to promise to a client.
What Labs is working on
Status is shown as it is. Nothing here is presented as finished until it is.
- productin development
NOIT
Market, competitive and creative intelligence for marketing and strategy teams.
NOIT started as an internal system for briefing, competitor tracking and audience analysis across social and advertising channels. It is being rebuilt on the same engineering principles as our client systems: evaluated outputs, traceable sources and human review. Public availability, scope and positioning are being defined.
- acceleratorinternal
Evaluation harness
A reusable way to build test sets from real cases and run them on every change.
Versioned datasets, automated and human-graded scoring, regression reports in the delivery pipeline. Used on client systems; not offered standalone.
- acceleratorinternal
Workflow orchestration patterns
Reference patterns for deterministic / AI / agent / human-approval steps.
Typed step contracts, approval gates, escalation with context and replayable traces, packaged so a new workflow starts from a proven skeleton.
- acceleratorinternal
Document pipeline components
Classification, extraction, validation and reviewer-queue components for document-heavy processes.
Confidence thresholds, correction capture that feeds evaluation, and traceability from document to posted record.
- researchexploration
Möbius
An AI concierge that helps a visitor describe a process and identify where an intelligent system could apply.
Möbius is a concept under evaluation, not a live product. Its architecture (UX, orchestration, qualification, privacy, human handoff) is documented; it will only appear on this site when it works reliably.
- Propose an experiment
Have a process where the outcome is uncertain and the learning would be valuable to both sides? Labs takes on a small number of these each year.
Talk to Labs
How Labs works
- Experiments
- Small, measured tests of models, retrieval strategies and agent designs on realistic data before they touch a client system.
- Benchmarks
- Internal comparisons of providers and approaches on the tasks that matter to our solutions: extraction, grounded answers, tool use.
- Reusable IP
- Accelerators that shorten the next build without locking clients into a proprietary platform.
- Open source
- Where a component is generic and useful, we intend to publish it. Nothing is published yet.
Next step
Have a problem worth an experiment?
Labs takes on a small number of exploratory engagements where the outcome is uncertain and the learning is valuable to both sides.