How we think about AI
Augment first. Automate honestly. Treat it as strategy.
Three principles that run through every engagement we take on. They are the difference between AI that ships and AI that stays in a deck.
01 · Augment first
Make the team three times faster, before anything else.
Most engagements start as 'give this team superpowers'. Better tooling, better dashboards, better internal copilots. From there, some functions grow into full automation. We do not lead with replacement, because that is rarely the strongest place to start.
02 · Honest about the consequences
If a job changes, we will say so upfront.
When you build an AI that handles 80% of customer support tickets, support hires slow or stop. We will not pretend that is neutral. We help you plan for the change, not paper over it.
03 · Strategic, not a tools purchase
AI lives with leadership, not procurement.
AI engineering is a business decision. Where to deploy it, what to keep human, what becomes a competitive moat. We work with directors and founders, not just IT.
Example build · Customer support agent
An LLM agent with bespoke tools and a knowledge base.
A customer message routes to an LLM core, which retrieves relevant context from a vector store and calls bespoke tools (orders, refunds, shipping). It responds with citations, or escalates to a human if confidence drops below threshold. Every step logged.
What we build
Six engagement patterns we ship every week.
01 · Agents
AI agents & autonomous workflows
Agents that own end-to-end processes, not just answer questions.
Bespoke agents built on Claude, OpenAI, or open-source models, with custom orchestration in .NET or Python. They classify, retrieve, decide, call APIs, and report back. We design the failure modes, the human-in-the-loop gates, and the audit trail so you actually trust them in production.
For businesses where the next ten hires are operational, not strategic.
What you get
- Agent design, prompt and persona engineering
- Tool-use schemas and API integration layer
- Orchestration, memory, and state management
- Confidence thresholds and escalation logic
- Observability, logging and audit trail
- Production deployment on your infrastructure
02 · Knowledge
RAG and knowledge platforms
Chat with your data. Internal copilots over your real source of truth.
Retrieval-augmented systems built over your documents, contracts, product catalogues, customer history, and internal knowledge. Vector databases, hybrid search, citation, and a UI your team will actually use. Not a chatbot bolted onto a help centre.
For organisations where the answer to most questions is buried in a dozen systems.
What you get
- Document ingestion, parsing and chunking pipelines
- Embedding strategy and vector database setup
- Hybrid retrieval (semantic + keyword) with re-ranking
- Conversational UI with citations and audit links
- Permission-aware access control
- Continuous evaluation and quality monitoring
03 · Platforms
AI-powered internal tools
Bespoke admin and ops platforms with AI features baked in.
Trading dashboards, merchandising tools, fulfilment workflows, customer review pipelines. Custom-built for how your business actually operates, with AI features integrated where they make the work meaningfully faster. Not generic SaaS.
For teams whose current admin lives in spreadsheets, Notion, and a Frankenstein of SaaS tools.
What you get
- Custom UI built on Next.js or .NET
- Bespoke data model and database design
- AI-assisted workflows and copilots
- Role-based access and audit logging
- Integration with your existing systems
- Hosting on Azure, AWS, or your own cloud
04 · Content
Content and creative automation
Product copy, ad creative, image variants and translation at scale.
For ecommerce brands with thousands of SKUs, content is operational debt. We build pipelines that research, write, review and approve copy in your brand voice, with humans in the loop at the right gates. Same approach extends to translations, ad creative variants, and image generation.
Brands with five-figure SKU counts, multilingual catalogues, or heavy content production needs.
What you get
- Multi-agent pipelines (research, write, review)
- Brand voice prompt engineering and style guides
- Translation and localisation workflows
- Image and creative variant generation
- Approval gates and human review flows
- Direct publishing to your CMS or PIM
05 · Service
Customer service automation
Smart agents that resolve, not deflect.
Support automation done properly: ticket triage, response drafting, knowledge retrieval, and escalation when confidence drops. Built on your help-desk platform, integrated with your order system, with full transparency on what the agent did and why.
Brands handling thousands of tickets a month where 60% of enquiries are repeat-pattern.
What you get
- Triage and intent classification
- Response drafting with retrieved context
- Order, refund and shipping tool integration
- Confidence thresholds and human escalation
- CSAT and resolution-rate monitoring
- Continuous prompt and knowledge tuning
06 · Glue
Process automation and integration
LLMs gluing systems together. Not Zapier.
Custom code where it matters. Robust failure handling, banking-grade audit trails, and the confidence to put real money or real customer data through it. We use n8n, Make, and similar tooling where they fit, and write custom services where they do not.
Operations that depend on integrations between ERP, CRM, OMS, finance and warehousing.
What you get
- System-to-system integration with LLM-driven transformation
- Document parsing and structured data extraction
- Workflow orchestration with retries and dead-letter queues
- Audit trails for compliance
- Cost monitoring and rate-limit handling
- Failover and graceful degradation
Example build · Multi-agent content pipeline
Specialist agents working a queue, with a supervisor watching.
A queue of products feeds three specialist agents in sequence. Researcher pulls supporting data, Writer drafts in your brand voice, Reviewer enforces tone and style. Rejections loop back. A supervisor agent monitors quality and retries on failure. Throughput: thousands of SKUs per day, hands-off.
Use cases
Instead of the next hire, build the work.
Every line of operational headcount has a price tag, a hiring lead time, and a churn rate. Some of those roles are inherently human. Others are repeat-pattern operational work that an agent can absorb permanently. Here is what we have built in place of hiring decisions.
Instead of
two more support agents
typical cost: £60k–£90k/yr
We build
Support agent with order, refund and shipping tools, escalating when confidence drops.
Outcome
Resolves 70%+ tier-1 tickets
Instead of
an outsourced content team
typical cost: £40k–£120k/yr
We build
Multi-agent pipeline writing product copy in your brand voice, with reviewer agent and human approval gate.
Outcome
12,000+ SKUs/month
Instead of
another ops manager
typical cost: £45k–£70k/yr
We build
Workflow agent triaging fulfilment exceptions, drafting supplier comms, and chasing late inventory.
Outcome
Hours of manual chasing eliminated
Instead of
a junior data analyst
typical cost: £35k–£50k/yr
We build
Internal chatbot over BigQuery, Shopify and ad platforms. Marketing and finance ask questions, get answers with SQL receipts.
Outcome
Self-serve answers, no ticket queue
Instead of
more hours from your finance team
typical cost: £25k–£50k/yr
We build
Invoice parsing, reconciliation and supplier-statement matching agent. Drafts the journal, you approve.
Outcome
90% match rate, manual review on the rest
Instead of
a translation agency
typical cost: £15k–£60k/yr
We build
Translation pipeline with brand-aware glossary, country-specific tone, and human review on critical content.
Outcome
Whole catalogue in 4 languages
Pricing
Scoped per engagement, fixed-price quote.
AI engineering does not fit neatly into a tier card. Every engagement starts with a discovery and scoping phase, followed by a fixed-price quote covering the build. Ongoing operations sit on a retainer once you are live.
2 to 3 weeks
Discovery & scoping
Map the work, prioritise the use cases, design the architecture, and quote the build.
Fixed-price
Build
A single agent or pipeline shipped to production in 6 to 12 weeks. Larger systems quoted accordingly.
Monthly retainer
Operate
Monitoring, prompt iteration, evaluation, model upgrades, and adding new capabilities as the system matures.
Trust, data, ethics
The bit most AI agency pages skip.
Most enterprise buyers we speak to ask the same four questions before signing. Here are the answers, in advance.
Your data stays your data
No training on your prompts or your documents. Contracts say so explicitly. We use enterprise tiers of model providers (Claude, OpenAI, Azure OpenAI) where the no-training clause is contractual, not aspirational.
Honest about model choice
We tell you exactly which models we are using and why, including when self-hosted open-source is the right answer for cost, privacy or IP reasons. No black-box "powered by AI" handwaving.
Failure modes by design
Every agent we build has clearly defined failure surfaces, rollback paths, and human-in-the-loop gates wherever a wrong answer has consequences. We design for the bad day, not just the good one.
Audit trails as standard
Banking-grade logging of every prompt, retrieval, tool call and response. You can always see what an agent did, why, and on whose authority. Compliance and security teams get the visibility they need.
Why us
Engineers who ship, not strategists who deck.
Most AI agency work today is consulting masquerading as engineering. We are the other way round: build-first, ship-fast, then advise on what to do next.
- 01
Built by engineers, not strategists
Our work ships in production, not in slide decks. Senior engineers do the work, with senior technical advisory drawn from a thirty-year career in investment banking systems architecture.
- 02
Augment-first, automate-when-it-makes-sense
We do not turn up with a redundancy plan. We start by making your existing team meaningfully more effective. Where automation makes sense, we do it transparently and on your terms.
- 03
Tied to actual revenue
Most of our work is for ecommerce and retail clients where the data infrastructure is also our problem. AI without clean data is theatre. We do both.
- 04
No long-term lock-in
We will not lock you into our platform. Every agent we build runs on your infrastructure, in your accounts, with your keys. If you want to take it in-house, we will hand it over and document everything.
FAQs
The hard questions, answered straight.
Are you trying to replace our team?
How do you decide what to automate?
What technology do you build with?
What about data privacy and IP?
How long does an engagement take?
Can we start small?
What kind of business is this for?
What does ongoing maintenance look like?
Pick a use case.
We will scope the build.
Tell us which operational role or workflow you would like to absorb. We respond within one business day with a written assessment, a recommended approach, and a fixed-price quote for a pilot.