What we build
AI products built to survive contact with production
Seven areas of work, each shipped to real customers rather than kept as a slide. Around 85% of engagements reach production. Where an area has been packaged as a product, it's listed on the card; the full details live on the Solutions page.
Agentic & GenAI product builds, 0 to 1
Discovery, MVP, and production build for GenAI and agentic products, from a first working prototype to something that survives real users and real data. Use-case discovery and readiness come first, so the build is sequenced against what the organization can actually execute.
Available as
Computer vision & retail intelligence
Detection, tracking, classification, re-identification, OCR, and multimodal vision, the same class of system behind 2 granted patents and deployments across 80%+ of the world’s top 200 retailers.
See the patents →ESG AI & decision intelligence
Metric extraction, NL2SQL chat over ESG data, and citation-verified reporting across BRSR, CSRD, GRI, ISSB and more, moving sustainability teams from static disclosures to a live decision layer.
Available as
NL2SQL, data & agent architecture
RAG, NL2SQL and Txt2SQL, vector databases, and agentic frameworks wired into existing data estates, so an agent’s answers stay traceable to the source data.
See project case studies →Revenue & presales agents
Agents grounded in your own customer profile and past proposals: finding accounts that match your ICP, drafting outreach, and assembling RFP and bid responses from what has already won.
See case studies →Responsible AI, governance & agent security
Bias and hallucination audits, RAG evaluation, red-teaming, and NIST AI RMF / ISO 42001 / HIPAA-aligned guardrails, plus runtime control for agents: policy decided at the point of action, before a tool call executes. Built in during architecture, not bolted on before an audit.
Available as
Fractional AI product management & training
Embedded PM and solution architect for teams without one yet, AI-native engineering practices for development teams, plus hands-on GenAI, deep learning, and vision training for 2,000+ practitioners across 64+ countries.
Available as
See one of these areas match your roadmap? Let's talk specifics.
Book a free call →What ties the work together
The CAL framework and Readiness over Awareness
Every build is judged against the CAL framework: Consistency, Accuracy, Latency. Before that, adoption is sequenced through Readiness over Awareness: aligning data readiness, AI capability, and business outcomes before a team commits to a build, rather than chasing awareness of a tool that isn't ready to be used.
Model reasoning handles interpretation and generation. Solution architecture handles data integration, evaluation, and guardrails. Responsible AI handles the governance layer that keeps both accountable, in production, not just in a demo.
Have a build in mind?
Bring the workflow, the data, or just the problem, and get an honest read on what it takes to ship it.