A laboratory for
applied intelligence.

Complex operational workflows — procurement, compliance review, due diligence — involve dozens of discrete decisions spread across specialists, systems, and days. Errors compound. Handoffs leak context. Nobody owns the whole trace.

A hierarchical agent system: a planner agent decomposes the workflow, specialist agents handle bounded subtasks, a supervisor agent validates intermediate outputs against policy. Every decision is logged, reversible, and explainable.

1. 01Workflow discovery + trace mapping
2. 02Agent topology design + tool interface
3. 03Iterative build with shadow-mode evaluation
4. 04Controlled rollout + human oversight handoff

Your team lives inside five different SaaS tools. Information dies in silos. People spend more time finding context than making decisions. Ripping and replacing the stack is not on the table.

A conversational copilot wired into existing APIs (CRM, ERP, knowledge bases, calendars). We don't replace tools — we orchestrate them. Scoped permissions, audit trails, and progressive autonomy gates keep humans in control.

1. 01Tool inventory + permission model
2. 02Intent taxonomy from real user logs
3. 03Build, red-team, instrument
4. 04Pilot group → broader deployment

Millions of events per day — transactions, sensor readings, system logs. Rule-based detection drowns in false positives. Real threats or failures hide in the noise until after they matter.

A layered pipeline combining statistical baselines, isolation forests for high-dimensional signals, and a lightweight neural model for sequence anomalies. Alerts are ranked, not binary. Operators tune thresholds without retraining.

1. 01Data characterisation + incident review
2. 02Baseline models + eval harness
3. 03Streaming integration + alert UX
4. 04Monitoring + drift response playbook

Operations run on spreadsheets and gut feel. Inventory bloats or stocks out. Capacity sits idle or buckles under surge. The data exists — nobody has turned it into decisions yet.

Hierarchical forecasting that respects the structure of the business — region, SKU, channel. Gradient-boosted models for tabular signals, probabilistic outputs for decision-making under uncertainty. Backtests against the last 24 months before anyone trusts the number.

1. 01Data audit + business hierarchy mapping
2. 02Baseline + champion/challenger eval
3. 03Decision tooling (not just forecasts)
4. 04Adoption support + retraining cadence

Your organisation's hardest-won knowledge lives in 40,000 PDFs, internal wikis, and email threads. New hires take a year to become useful. Generic LLMs hallucinate because they've never seen your documents.

A retrieval pipeline tuned to your documents, not a generic embedding. Hybrid search (semantic + lexical), reranking, citation-first outputs. An evaluation harness that measures accuracy against a golden set of real user questions — not benchmarks.

1. 01Corpus audit + golden question set
2. 02Retrieval evaluation harness
3. 03Iterative tuning + generation layer
4. 04Interface + feedback loops

Leadership knows AI matters but doesn't know where to start — or worse, is being sold expensive transformation by vendors with their own agenda. You need a clear-eyed assessment that's free of sales incentive.

A focused audit of data maturity, technical capability, and organisational readiness. Every identified opportunity is evaluated by expected value, implementation risk, and time-to-signal. The output is a sequenced roadmap, not a wishlist.

1. 01Stakeholder interviews + data landscape
2. 02Opportunity identification + triage
3. 03Prioritisation against value & risk
4. 04Roadmap delivery + C-suite briefing

A vendor wants $400K/year for their AI product. Your engineering team says they can build it in three months. Marketing wants ChatGPT Enterprise. Nobody can answer which path is actually right — and the wrong choice costs seven figures over three years.

A structured evaluation across total cost of ownership, strategic fit, vendor lock-in risk, and build feasibility. We benchmark vendor offerings against a realistic internal build estimate. Recommendations are documented with the reasoning trail — so the decision can be defended later.

1. 01Requirements definition + decision criteria
2. 02Vendor landscape scan + build estimate
3. 03Comparative evaluation + scenarios
4. 04Recommendation + implementation pathway