AI & Automation
Intelligence built into every workflow.
We embed AI and automation deep into your enterprise operations — from CRM pipelines to document workflows — so your teams spend time on what only humans can do.
45+
Models in production
62%
Median manual-effort cut
6 wks
To first shipped use case
100%
Deployments with eval gates
Why pilots stay pilots
Three reasons the demo impressed and the rollout never happened
Almost every organisation we meet has run an AI pilot. Far fewer have one in production carrying real load.
Nobody defined 'good enough'
The demo was judged by whether it felt impressive. Without an agreed accuracy bar measured on your own data, there is no threshold at which it can be approved for production.
- No evaluation set built from real cases
- Quality assessed by anecdote
- No regression check when the model changes
The unhappy path was never designed
Models are wrong sometimes. If there is no review queue, no confidence threshold and no escalation, the first bad output becomes an incident and the system gets switched off.
- No human-in-the-loop for low-confidence cases
- No audit trail of what the model decided and why
- No fallback when the provider is degraded
Data governance stopped it at the gate
Legal asks where the data goes, who trained on it, and how a subject-access request is answered. The pilot has no answers, so it never leaves the sandbox.
- Prompt and output retention undefined
- PII crossing a boundary nobody approved
- No record of model version per decision
Capabilities
Enterprise AI capabilities, deployed responsibly
We bring current AI into your existing stack — securely, compliantly, and at enterprise scale.
Large language model integration
Deploy frontier and open-source models inside your enterprise with retrieval pipelines grounded in your own content and evaluation gating every release.
- RAG over your corpus
- Prompt and output logging
- Model-version pinning
Intelligent process automation
Agents that handle document processing, approvals and data entry, with confidence thresholds routing anything uncertain to a human queue.
- Human-in-the-loop review
- Deterministic fallbacks
- Full decision audit
Predictive analytics & ML
Custom models for demand forecasting, churn prediction and revenue intelligence, delivered into the systems where the decision is made.
- Feature store
- Drift monitoring
- In-product delivery
Conversational AI
Assistants integrated with your CRM and knowledge base, with multilingual support and a clean escalation path to a human agent.
- Grounded answers only
- Escalation on low confidence
- Conversation analytics
Computer vision
Document OCR, identity verification, quality-control inspection and visual analytics pipelines running against your throughput.
- Extraction with confidence
- Quality inspection
- Edge or cloud inference
Evaluation & governance
The part most suppliers skip. Evaluation sets from your data, regression gates on every change, and an audit trail per decision.
- Golden evaluation set
- Regression gates in CI
- Per-decision provenance
Under the hood
Four layers between a user question and a trustworthy answer
The model is one component. Most of the engineering that makes a system dependable sits in the three layers around it.
Knowledge
Ingestion and chunking
Documents parsed, chunked and enriched with the metadata retrieval needs.
Vector and keyword index
Hybrid retrieval, because pure semantic search misses exact identifiers.
Permission-aware retrieval
The index respects who is asking, so answers never leak across roles.
Freshness pipeline
Source changes reflected on a known cadence, with staleness visible.
Reasoning
Model routing
Cheap models for easy cases, frontier models where the task earns it.
Grounded generation
Answers cite retrieved sources, and refuse when retrieval returns nothing.
Tool use
Structured calls into your systems rather than hallucinated values.
Provider fallback
A degraded provider fails over rather than failing the workflow.
Control
Confidence thresholds
Low-confidence outputs routed to a review queue, not to the customer.
Human-in-the-loop
Reviewer decisions captured as training signal for the next iteration.
Guardrails
Input and output filtering for PII, injection and out-of-scope requests.
Decision audit
Model version, prompt, retrieved context and output stored per decision.
Evaluation
Golden set
Real cases with agreed correct answers, owned by your domain experts.
Regression gates
No prompt or model change ships without clearing the previous score.
Online monitoring
Live quality tracked against sampled human review, not just uptime.
Cost per outcome
Token spend attributed to the business action it produced.
We will not ship an AI feature without an evaluation set and an agreed accuracy bar. If the use case cannot be evaluated, that is a finding worth having before the build rather than after it.
Our approach
How we keep AI work honest
Selection
Most AI ideas should not be built
We score candidate use cases on value, data readiness, tolerance for error and how the outcome will be measured. The majority fail on data readiness or on nobody being able to say what 'correct' means, and it is much cheaper to learn that in a workshop.
- Use cases scored against a published rubric, with you in the room
- Data readiness assessed before any model work begins
- Error tolerance agreed explicitly, per use case
- Anything without a measurable outcome is parked, not built
Evaluation
An accuracy bar agreed before the build
Every engagement starts by building an evaluation set from your real cases, with correct answers agreed by your domain experts. That set becomes the gate: prompts and models change constantly, and the score is what tells you whether a change was an improvement.
- Golden set built from genuine historical cases
- Target accuracy agreed and written down before development
- Regression gates in CI on every prompt and model change
- Live quality sampled and reviewed after launch, not assumed
Governance
Designed for the day it is wrong
A model in production will produce a bad output eventually. Systems that survive that are the ones where it was anticipated — confidence routing, a review queue, a full audit trail, and a switch that turns the feature off without taking the workflow down.
- Confidence thresholds routing uncertain cases to humans
- Model version, prompt and context stored per decision
- Data residency and retention agreed with your legal team first
- Feature-flagged rollout with a tested kill switch
Use-case selection
Where AI pays, and where it quietly does not
The rubric we use in the selection workshop. Most organisations arrive wanting the bottom row and leave building the top two.
| Use case | Data readiness | Error tolerance | Measurable outcome | Verdict |
|---|---|---|---|---|
| Document extractionStrong fit | High — historical documents already labelled by outcome | Medium — review queue absorbs uncertainty | Direct: hours of manual keying removed | Build first |
| Lead scoringStrong fit | High — CRM history is the training set | High — a wrong score costs a call | Direct: conversion rate by score band | Build early |
| Internal knowledge assistant | Medium — depends on documentation quality | Medium — grounded answers with citations | Indirect: time-to-answer, needs instrumenting | Build after the corpus is cleaned |
| Demand forecasting | Medium — needs clean ERP history and external signal | Low — decisions commit inventory spend | Direct, but slow to prove | Build with a long parallel run |
| Customer-facing autonomous agent | Varies — usually overestimated | Very low — errors reach the customer | Direct but reputationally risky | Only behind human review |
| Fully automated decisioning | Often insufficient for the claim being made | None — regulated or irreversible outcomes | Hard to attribute | Usually park it |
Document extraction
Strong fit- Data readiness
- High — historical documents already labelled by outcome
- Error tolerance
- Medium — review queue absorbs uncertainty
- Measurable outcome
- Direct: hours of manual keying removed
- Verdict
- Build first
Lead scoring
Strong fit- Data readiness
- High — CRM history is the training set
- Error tolerance
- High — a wrong score costs a call
- Measurable outcome
- Direct: conversion rate by score band
- Verdict
- Build early
Internal knowledge assistant
- Data readiness
- Medium — depends on documentation quality
- Error tolerance
- Medium — grounded answers with citations
- Measurable outcome
- Indirect: time-to-answer, needs instrumenting
- Verdict
- Build after the corpus is cleaned
Demand forecasting
- Data readiness
- Medium — needs clean ERP history and external signal
- Error tolerance
- Low — decisions commit inventory spend
- Measurable outcome
- Direct, but slow to prove
- Verdict
- Build with a long parallel run
Customer-facing autonomous agent
- Data readiness
- Varies — usually overestimated
- Error tolerance
- Very low — errors reach the customer
- Measurable outcome
- Direct but reputationally risky
- Verdict
- Only behind human review
Fully automated decisioning
- Data readiness
- Often insufficient for the claim being made
- Error tolerance
- None — regulated or irreversible outcomes
- Measurable outcome
- Hard to attribute
- Verdict
- Usually park it
Delivery
From candidate list to production
Each phase can end the engagement without waste. The evaluation set survives whatever you decide.
- 01
Week 1
Use-case workshop
Candidates scored against the rubric with your domain experts in the room. Output is a ranked shortlist and an honest list of what should not be built.
- 02
Week 2–3
Evaluation set
Real historical cases collected, correct answers agreed, and the accuracy bar written down. This is the contract for everything that follows.
- 03
Week 4–6
Grounded prototype
Retrieval, model routing and guardrails built against the evaluation set, running on your data in your environment. Scored, not demoed.
- 04
Week 7–10
Production hardening
Review queues, audit trail, cost controls, provider fallback and the kill switch. Feature-flagged rollout to a limited cohort first.
- 05
Ongoing
Monitoring and iteration
Live quality sampled against human review, drift watched, and the evaluation set grown from the cases the system got wrong.
What you get
What you keep, including if you stop
The evaluation set is the most durable artefact here. It outlives any particular model, provider or prompt.
Selection
- Scored use-case rubric with the reasoning recorded
- Data-readiness assessment per candidate
- Explicit list of what we recommend not building
- Cost-per-outcome model for the shortlist
Evaluation
- Golden evaluation set owned by your domain experts
- Agreed accuracy bar, written down before the build
- Regression harness runnable in your CI
- Benchmark results across candidate models
Production
- Retrieval and inference pipeline as code in your repository
- Review queue, audit trail and kill switch
- Data residency and retention documented for legal
- Monitoring dashboards for quality, drift and spend
Toolchain
What we build AI systems on
Provider-agnostic by construction. Model choice is a routing decision, not an architectural commitment.
Models
Claude, GPT and Gemini via API, with Llama and Mistral self-hosted where residency or cost requires it.
Orchestration
LangChain and LlamaIndex where they earn their keep, plain SDK calls where they do not. Temporal for long-running agent work.
Retrieval
pgvector, Pinecone and OpenSearch, with hybrid keyword and semantic ranking rather than embeddings alone.
Evaluation
MLflow and Weights & Biases for tracking, custom harnesses for domain scoring, OpenTelemetry for trace-level cost attribution.
Engagement models
Ways to start with AI
Nobody should sign a build before the use case has been scored and the accuracy bar agreed. The first two options exist for exactly that reason.
Use-case workshop
Fixed fee
One week
A scored shortlist and a candid list of what not to build, with your experts in the room.
- Candidates scored against a published rubric
- Data readiness assessed per use case
- Cost-per-outcome modelled
- Findings yours whatever you decide
Evaluated prototype
Project
Roughly six weeks
One use case built against a real evaluation set and scored, not demoed.
- Golden evaluation set you keep
- Working prototype on your data
- Measured against the agreed accuracy bar
- Honest verdict on production readiness
Production and operate
Retainer
Monthly, with quality SLOs
Hardening, rollout and ongoing operation with quality monitored against human review.
- Review queues, audit trail and kill switch
- Live quality sampling and drift monitoring
- Model and prompt iteration under regression gates
- Spend attribution per business outcome
Questions
The questions worth asking about enterprise AI
Including the ones a supplier who wants the contract would rather you did not ask.
Trust and accuracy
How do we know it is accurate enough?
Because you set the bar before the build, on an evaluation set drawn from your own historical cases with correct answers agreed by your own experts. Accuracy then stops being a matter of opinion: every prompt or model change is scored against that set, and anything that regresses does not ship. If a use case cannot be evaluated this way, we will tell you it is not ready to build.
What happens when the model gets something wrong?
It gets caught by a confidence threshold and routed to a human review queue, and the reviewer's decision is captured as signal for the next iteration. Every decision stores its model version, prompt and retrieved context, so an incident is investigable rather than mysterious. There is also a tested kill switch that disables the feature without taking the surrounding workflow down.
Will it hallucinate?
Any language model can. The mitigations are grounding answers in retrieved sources with citations, refusing to answer when retrieval returns nothing relevant, and using structured tool calls into your systems rather than letting the model produce values from memory. Those reduce it substantially; they do not reduce it to zero, which is why the review queue exists.
Data, cost and lock-in
Where does our data go?
Wherever you and your legal team agree, and it is settled before any build starts. Options range from API providers with zero-retention terms through to fully self-hosted open-weight models in your own tenancy. Residency, retention and training-use terms are documented per component so a subject-access request has an answer.
Are we locked into one model provider?
No, deliberately. Model choice sits behind a routing layer, so switching provider is a configuration change plus an evaluation run rather than a rebuild. This matters more than it sounds — pricing and capability in this market change on a timescale of months.
How do we control what this costs to run?
Model routing sends easy cases to cheap models and reserves frontier models for work that earns it, and token spend is attributed to the business outcome it produced rather than arriving as one line. You get a cost-per-outcome figure during the prototype, which is usually the number that decides whether the use case is worth shipping.
Start with the use case, not the model.
A one-week workshop that scores your AI candidates against data readiness and measurable outcome — including the ones we would tell you not to build.