Built for production, not demos
Every system ships with integrations, monitoring and review points, so it keeps working after launch.
Integrations
Connected to ERP, CRM, email, ticketing and document stores through secure APIs.
Semantic search
Transformer and vector-embedding search that understands intent, not just keywords.
Document AI
Extraction, classification and vectorization of documents, images and plans.
ML models
Recommendations, forecasting, lending decisions and fraud detection trained on your data.
Human review
Clear points where people approve, correct or override what the AI proposes.
Monitoring & evaluation
Accuracy, latency and cost tracked in production, with alerts when quality drifts.
From one workflow to many
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Pick the workflow
One process, its systems and the metric it should move.
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Build the pilot
A working agent or model on your real data, inside the real tool.
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Harden it
Security, integrations, evaluation and fallbacks for production.
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Roll it out
More teams, more processes, the same monitoring.
Assist, answer, act, automate.
Most clients use all four, matched to each process and how much autonomy it can safely take.
Assist
AI drafts. People decide. Copilots for proposals, summaries and code review.
Answer
Instant answers from your own documents, with the source shown for every answer.
Act
Agents that finish multi-step work across your tools, with a person approving where it matters.
Automate
Rules that run the repetitive, high-volume work, every time.
Results from production systems
E-Commerce, Australia
Semantic product search, personalised recommendations and retail media analytics.
Read the case studyReal Estate, Norway
A multi-model deep-learning pipeline that vectorizes floor plans in 3.2 seconds per image.
Read the case studyMobile financial services, Bangladesh
AI-driven lending and an ML fraud engine inside a finance app used by millions.
Read the case studyQuestions we hear most
Still unsure? Ask an engineer.
An assistant drafts or answers and a person acts on it. An agent completes several steps across your tools itself, with approval points you choose.
No. We design each system around the people who own the process: AI takes the repetitive steps, people keep the decisions.
Whatever fits the job and your data rules: open-source models, the major cloud providers, or models we train, such as segmentation models for floor plans.
Evaluation sets built from your own cases, monitoring in production, and human review wherever a mistake would be costly.