GenAI Service Layer
When every team needs LLMs, you need infrastructure that turns chaos into reliability. A single managed surface for 100+ production use cases, multi-cloud, multi-vendor, model-agnostic.
- Smart routing across providers and model tiers
- Prompt + response caching for latency and cost
- Observability, audit trails, and policy enforcement
- Cost governance, quota management, and chargeback
- Drop-in client SDKs for any internal team
- Model-agnostic abstraction over OpenAI, Anthropic, Google, AWS, and more
Agent Service Layer
Agents aren't a feature you ship once. They're a class of system that has to be built, evaluated, validated, and operated. Built atop the GenAI Service Layer, this framework standardises every step of that lifecycle.
- Standardised agent harness, lifecycle, and orchestration
- Reusable tool library with parallel/agentic call support
- Built-in memory, retrieval, and state management
- Human-in-the-loop checkpoints and approval flows
- Evaluation harness with regression test suites
- Full idea-to-prod pipeline (build, eval, validation, load test, deploy) in 2 weeks or less, vs 16 weeks before
Multi-agent customer chatbot
The first proof that the Agent Service Layer scales: a customer-facing chatbot handling 85,000+ queries per week, with CSAT lifted from ~50% to 80%+ over the program lifecycle.
- Multi-agent orchestration with specialised sub-agents
- Parallel tool calls and agentic delegation
- Human-in-the-loop escalation on high-stakes flows
- Persistent interaction + knowledge memory across turns
- Multi-step transactional workflows (booking, modification, support)
- Real-time knowledge retrieval from internal sources
Industry partnerships
The major labs ship faster than anyone can keep up with. Active engagement and pre-release evaluations with industry-leading AI organizations turn that velocity into enterprise advantage: co-shaping roadmaps, evaluating frontier capabilities pre-release, and translating research into production. Headlined by an industry-first major-airline × OpenAI collaboration covering enterprise chatbot enhancement, multimodal customer servicing, and staff AI assistance, all built on the GenAI + Agent Service Layers.
- OpenAI: Industry-first major-airline collaboration covering enterprise chatbot, multimodal customer servicing, and staff AI assistance.
- Anthropic: Enterprise readiness collaboration around Claude in production, agentic systems, and safety-first deployment patterns.
- Google: Frontier model access, multimodal capabilities, and platform-level integration via Vertex AI.
- AWS: Bedrock-based multi-vendor model access, enterprise compliance, and infrastructure scaling.
- Salesforce: Agentforce, Einstein, and customer-engagement-layer GenAI integration.
Internal engagements
The hardest part of enterprise AI is not the model, it's the people. Personally lead and design programs that move the organization from "GenAI curious" to "GenAI capable", spanning individual contributors to the C-suite.
- Hands-on training and workshops for engineering teams
- Upskilling programs for technical and product teams adopting GenAI
- 1:1 coaching for senior engineers and architects on agentic design
- Mentorship for early-career engineers and technical leads
- Executive briefings and advisory for senior leadership and the C-suite
- Train-the-trainer scaling across business divisions
DocIQ
Multi-modal, VectorDB-less, agentic RAG for high-accuracy enterprise document Q&A. Built to handle the documents that ordinary RAG stacks fail on: long contracts, regulatory filings, technical specs with tables and diagrams.
- Multi-modal understanding: text, tables, charts, diagrams, scans
- Agentic planner-executor-verifier loop for grounded answers
- VectorDB-less retrieval, structured indexing over dense vectors
- Configurable provenance, citation, and confidence reporting
- Rebuilt and renamed from earlier OneSearch prototype
Tacit
Local-first knowledge digitisation. Captures the unwritten expertise that lives in people's heads and turns it into queryable structured knowledge, without ever leaving the device.
- Conversational interview-style knowledge capture
- Structured extraction into queryable knowledge graphs
- Fully on-device LLM execution for IP and privacy protection
- Designed for regulated industries and sensitive expert domains
- Successor to the earlier LLMwiki experiment
Voice AI for internal staff operations
Legacy speech-to-text-to-speech pipelines compound errors and add latency at every stage. Native end-to-end streaming voice replaces them with real-time, low-latency, prosody-aware conversation for internal staff in high-stakes operational workflows.
AI-powered travel-planning experience
GenAI search + smart flight recommendation, built atop the GenAI Service Layer. Doubled customer satisfaction on AI search; the recommendation surface averages 2,000 queries/day with click-through up from 23.8% to 34.4% in three months.
More on the workbench.
The above is a snapshot of the most visible work, not the full surface. Other ongoing threads and topics include:
- GenAI content authenticity: Provenance and synthetic-media detection via C2PA signing and SynthID-style watermarking, applied across internal documents, customer communications, and outbound assets.
- Enterprise process redesign: Rewiring core operational processes end-to-end around data-driven decisions and AI-enabled pipelines, not bolting AI on top of legacy workflows.
- Adversarial robustness and guardrails: Defence-in-depth against prompt injection, jailbreaks, data exfiltration, and model-targeted attacks. Continuous red-teaming, not one-off audits.
- AI governance and policy operationalisation: Translating regulatory frameworks and internal policy (EU AI Act, NIST AI RMF, model-card and data-lineage requirements) into enforceable runtime controls at the platform layer.
- Evals at enterprise scale: Reusable evaluation harnesses, regression suites, and trust telemetry that hold up across 100+ production use cases.
- Cost and capacity economics: Multi-vendor capacity planning, tier-based model routing, and unit-economics modelling for GenAI workloads at production scale.
- Knowledge graphs + LLM hybrid systems: Structured retrieval and reasoning over enterprise knowledge, complementing or replacing pure vector RAG where precision matters.
- On-device and edge inference: Local-first GenAI for sensitive, regulated, or low-connectivity workflows where data cannot leave the device.
For a specific topic or collaboration not listed here, the fastest way to start a conversation is LinkedIn.