⟶ Practice / 02
11anonymised engagements shipped
AI that moves a KPI, not a demo.
Generative AI, autonomous agents and predictive systems wired into the parts of your business that actually matter.
Outcomes · Selected proofs
Claims auto-classified
Veridia
Saved per ops analyst
Atlas
First production model
Internal
What we do
Most AI work stalls at the proof-of-concept. We build production AI services — generative AI, AI agents, RAG copilots, document and email intelligence, predictive ML and MLOps — with evaluation, guardrails and ownership of the operational metric that matters. The differentiation isn't the model; it's the custom layer around it.
Capabilities
Generative AI & LLM apps
Custom LLM applications — copilots, content engines, conversational surfaces and structured-output workflows — model-agnostic by design, with the evaluation harness, prompt registry, model-routing layer and observability your team can actually own once we leave.
AI agents (autonomous)
Goal-pursuing agents that perceive, reason and act across your APIs, CRM, ERP and knowledge base. Multi-agent orchestration, tool-use, memory, human-in-the-loop checkpoints and rollback paths — engineered with the same rigour as the rest of your production estate, not a notebook left running.
RAG & knowledge retrieval
Retrieval-augmented systems grounded in your corpus — chunking strategies, hybrid lexical + vector retrieval, re-ranking, citation enforcement, confidence thresholds, access-scoped indexes and graceful fallbacks. Built on pgvector, Pinecone, Weaviate or Qdrant; every answer traceable back to the source document.
Document & email intelligence
Extraction, classification and routing for invoices, contracts, claims and inbound email at production volume — with structured outputs and validation.
Predictive ML
Forecasting, churn, fraud, scoring and recommendation models on your data — classical ML where it outperforms LLMs, and where economics demand it.
Computer vision
Detection, classification and OCR for shop floor, retail, logistics and field-ops — on-device, edge or cloud depending on latency budget.
Conversational AI & voice
Voice and chat assistants for support, sales and field-ops — built on Whisper, ElevenLabs and frontier LLMs with telephony and CRM hooks.
MLOps & guardrails
Evaluation suites, regression tests, observability, cost controls, PII redaction and safety guardrails so AI features stay reliable in production.
Learning paradigms
Three paradigms. One unified practice.
When the data is labelled and the answer is knowable — fraud scores, demand forecasts, churn risk, underwriting. Classical ML still wins on cost, latency and explainability; we use it where the regulator or the CFO needs receipts.
When you don't yet know what 'normal' looks like — segmentation, anomaly detection, embeddings, semantic search. The shape of the data becomes the product surface and feeds every downstream copilot.
When the task is open-ended — drafting, reasoning, multi-step tool use. LLMs and agents close the loop with citations, evals and human-in-the-loop on the high-risk steps. This is the decade ahead; we engineer for it, not chase it.
How agents work
How AI agents work — perceive, decide, act.
Perceive
Gather signals from APIs, databases, queues and real-time streams. Build a working picture of state before deciding.
Decide
Reason over goals, weigh options and pick the action most likely to move the objective — not just match a static rule.
Act
Execute through tools, APIs and workflows. Close the loop, log the outcome and feed it back into the next perception cycle.
Generative AI
Generative AI use cases
that earn their keep.
Generative AI demos well and ships poorly. The gap is engineering. We build for the second part.
Internal copilots
Role-specific assistants for sales, support, finance and ops — grounded in your stack, scoped by permission.
Customer-facing assistants
Branded chat and voice surfaces with citation-backed answers, escalation paths and full audit trails.
Code & developer copilots
Repo-aware assistants for engineering teams — code review, migration and documentation, wired to your standards.
RAG on your knowledge
Searchable, citable answers across SharePoint, Confluence, Notion, Drive and your data warehouse — without leaking access.
AI tech stack
Our AI tech stack — research to production.
Foundation models
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- Qwen
Orchestration & frameworks
- LangChain
- LlamaIndex
- Haystack
- DSPy
- CrewAI
- LangGraph
Vector & retrieval
- pgvector
- Pinecone
- Weaviate
- Qdrant
- Elastic
- Postgres FTS
ML & deep learning
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- Hugging Face
- ONNX
Serving & MLOps
- FastAPI
- Triton
- vLLM
- MLflow
- Weights & Biases
- LangSmith
Workflow & integration
- Temporal
- Airflow
- n8n
- Zapier
- Whisper · ElevenLabs
- Twilio
Agentic protocols & runtime
- Model Context Protocol (MCP)
- Agent-to-Agent (A2A)
- OpenAI Agents SDK
- AWS Bedrock Agents
- Vertex AI Agents
- LangGraph Platform
Evaluation & safety
- Ragas
- TruLens
- Promptfoo
- Guardrails AI
- NeMo Guardrails
- Lakera
Approach
How we run engagements. Predictable rhythm, senior owners.
A short cadence, demos every two weeks, one accountable lead — no theatre, no surprises.
- 01
Frame
Pick one business metric. Define what 'good' looks like. Build a baseline before any model.
- 02
Prototype
Ship a thin vertical slice in 2–3 weeks. Test against real data, not synthetic.
- 03
Harden
Add evaluation, guardrails, observability and human-in-the-loop where risk demands it.
- 04
Operate
Monitor drift, cost and accuracy. Iterate on the metric — not on the demo.
What you receive
Concrete artifacts. Nothing hand-wavy.
Signed off at go-live. Yours to own — source, runbooks and rights, no lock-in by obscurity.
Industry plays
AI by industry — where it pays off.
Insurance
Claims classification, commission engines, policy intelligence
72% claims auto-classified
Logistics
Document intelligence, freight ETA prediction, exception routing
11 hrs/wk per analyst
Retail & eCommerce
Catalog enrichment, search relevance, conversational commerce
3× faster content ops
Healthcare
Clinical document extraction, intake copilots, prior-auth automation
On-prem-ready, HIPAA-aligned
SaaS
Embedded copilots, semantic search, usage-driven recommendations
Days from spec to live
Professional services
Knowledge copilots, proposal generation, time-capture automation
60% workload reduced
Media
Editorial copilots, content tagging, localisation and asset intelligence at programmatic scale
Newsroom velocity, kept
Automotive
In-vehicle assistants, warranty intelligence, predictive maintenance and dealer copilots
Warranty cost trimmed
FMCG
Demand sensing, trade-promo optimisation, shelf-image vision and brand voice copilots
Forecast accuracy lifted
Engagement models
Three ways to work with us. One standard.
Project-based for fixed scope. Time & material for evolving work. Dedicated developers when you need senior capacity embedded with your team. Every engagement is shaped to your goals — we’ll recommend the right fit on the first call.
Engagement model
Project-Based
Fixed scope, fixed outcome. We define the work, agree the milestones, and own delivery end-to-end.
- Timeline
- Fixed scope
- Best for
- Well-defined builds, migrations and one-off implementations.
- ▸Senior-led discovery & sign-off
- ▸Defined milestones with demos
- ▸UAT, training & handover
- ▸Post-launch warranty
Engagement model
Time & Material — Agile
A senior squad billed by sprint. Scope flexes as you learn; cadence and quality stay constant.
- Timeline
- Sprint cadence
- Best for
- Evolving scope, continuous product work and discovery-led builds.
- ▸Dedicated senior squad
- ▸Two-week sprints with demos
- ▸Transparent burn-up reporting
- ▸Rolling roadmap
Engagement model
Dedicated Developers
Hire named senior engineers full-time, embedded with your team. You set priorities; we own quality.
- Timeline
- Ongoing
- Best for
- In-house teams that need senior capacity without the hiring lift.
- ▸Named senior engineers
- ▸Embedded in your stand-ups & tools
- ▸Tech-lead oversight included
- ▸Monthly performance review
Guardrails & governance
AI governance & guardrails — built so legal signs.
Evaluation harness
Per-feature eval suites that block regressions on every deploy — not optional, not after-the-fact.
Citations & provenance
Every generated answer traces back to the source. Auditors and end users see the receipts.
PII redaction & access control
Sensitive data masked in-flight; retrieval scoped by user permission, never by hope.
Private deployment
Self-hosted, VPC or on-prem options with no data leaving your perimeter — required by regulators, increasingly preferred by everyone.
Cost & drift monitoring
Per-feature spend, quality drift and latency dashboards — so AI stays operable, not just live.
Why us
Why senior buyers pick us — and stay.
Seniors only, on the work
The people you meet in the pitch are the people who write the code. No bait-and-switch to juniors after kickoff.
One accountable lead
Every engagement has a single tech lead who owns scope, timeline and quality — and answers their own emails.
Built to be left
We ship documentation, runbooks and training so your team can take it forward without us. No lock-in by obscurity.
AI across the practice
We do AI alongside everything else we ship.
AI isn't a separate department here. It's the layer that compounds every other discipline — ERP, product, infrastructure and growth.
Related work
Where it shipped.
Industries shipped in: Insurance · Logistics & 3PL · E-commerce
Veridia Assurance →
CRM, Subscriptions and Commission Management unified for a European insurer — renewals, claims and broker payouts running on Odoo end-to-end.
Atlas Logistics Group →
A multi-warehouse 3PL operator replaced fragmented WMS and billing tools with Odoo — inbound, outbound, billing and client portals unified on one stack.
Souqora Online Marketplace →
A 60,000-SKU electronics marketplace migrated from a brittle Magento extension to Odoo Enterprise on-premise — inventory, fulfilment and an AI-assisted product catalogue, all on one stack.
Avg manual workload reduced
Production AI deployments
Time-to-first-value
Questions
Things people ask.
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