Data & AI.your data, working for you. No hallucination theatre.
For UK businesses whose data lives in spreadsheets nobody trusts, or whose day is filled with document shuffling an AI could handle.
The Data & AI pillar turns your business’s own data into automated daily action. Dashboards that show what’s actually happening, not what someone hopes is happening. Pipelines that move data between systems without a person in the loop. LLM-augmented internal workflows for the document-shaped parts of the day, built with structured outputs and human-in-the-loop where it matters. No model lock-in. Data stays on UK infrastructure you own. Scoped per engagement, usually slotted under an Engineering or Strategy build.
Fixed fees, no VAT (Orchestrix is not VAT-registered). See FAQ for typical price bands.
Data & AI engagements give a UK small business three things: dashboards that turn raw operational data into a daily picture of the business, pipelines that move data between systems without a person in the loop, and LLM-augmented workflows that handle the document-shaped grunt work (CV parsing, invoice extraction, support-ticket triage) with a human-in-the-loop where it matters. Built on infrastructure you own, fixed-fee, scoped after the audit.
Engineering with evidence.How does Data & AI differ from Engineering?
Most Engineering builds touch data (every automation pipeline moves it). Data & AI is the pillar where the data isthe deliverable. The build doesn’t end with a script that runs; it ends with someone in the business being able to answer a question they couldn’t answer before, repeatedly, without asking the developer.
The AI half is the same shape. Not a chatbot bolted onto the homepage. Not “ask our AI agent anything.” LLMs used inside the operational workflow, with structured outputs, validation, observability, and a human in the loop on anything load-bearing. The model is one component of an engineering system, not the product.
Orchestrix runs production LLM workflows for its own outreach: 26,000 UK companies enriched through an open-weight model pipeline (Gemma family via OpenRouter), local sentence-transformer embeddings, structured-output prompts, cost-per-1k-tokens kept honest. The same approach is what shows up in client builds. Reference architecture, not experiment.
“If the right answer doesn’t need an LLM, the right answer doesn’t use an LLM.”
Three patterns
the bureau builds.
Operational dashboards
Metabase, Power BI, or a custom React dashboard depending on the audience. Connected to the data you already produce: orders, jobs, tickets, time entries. Refreshed automatically. The kind of thing a registered manager checks at 8am, not a quarterly board pack.
- →Metabase / Power BI / custom
- →Scheduled refresh
- →Self-hosted, UK residency
- →Role-based access
Data pipelines
Scheduled jobs that pull from one system (Xero, HubSpot, your CRM), transform, and push to another (a warehouse, a dashboard, a downstream tool). Error-handling, observability, alerts when something breaks. The script you wish someone had written six months ago.
- →Python / SQL
- →Postgres or SurrealDB
- →Monitoring & alerts
- →Idempotent reruns
LLM-augmented workflows
An LLM as one component inside an engineering build. CV parsing, invoice extraction, ticket classification, document summarisation. Structured outputs, validation against the source, human-in-the-loop on anything that matters. Runs on infrastructure you own.
- →Open-weight or proprietary models
- →Structured-output prompts
- →Human-in-the-loop UI
- →Observability & cost tracking
Is this right
for you?
- →Your team can’t answer simple operational questions without exporting to Excel
- →Data lives in three tools that don’t talk to each other and someone is bridging the gap by hand
- →There’s a document-shaped job (CVs, invoices, tickets, PDFs) eating hours every week
- →You want AI built into your operations, not a chatbot bolted onto the homepage
- →Data residency and IP ownership matter; you want it on infrastructure you control
- ×You want a public-facing AI chatbot. That’s not what this pillar does
- ×You want a generic ‘AI strategy’ deck without anything built
- ×Your data is genuinely all in one well-instrumented SaaS already. Use its native reports
- ×You want a vendor that signs an enterprise data-sharing agreement covering 1M+ users. The solo-operator model isn’t enterprise-scale
Questions the bureau gets.
- What counts as ‘AI’ here?
- Not marketing-speak. The bureau uses LLMs (large language models like the open-weight Gemma family or proprietary models via OpenRouter) where they’re genuinely useful: classifying inbound emails, extracting structured data from PDFs, summarising long documents, drafting first-pass responses for a human to approve. AI is a tool inside an engineering build, not a product the bureau sells separately. If the right answer doesn’t involve an LLM, the right answer doesn’t involve an LLM.
- Do you build dashboards in Power BI, Looker, Metabase, something else?
- The tool depends on the data, the users, and what already exists. Metabase is the default for teams without an existing BI stack: cheap to self-host, easy to maintain, doesn't lock you in. Power BI makes sense if you're already inside Microsoft 365 and the finance team lives there. Custom React dashboards make sense when the data lives in a bespoke database and the audience is internal operators, not analysts. Decision happens during the audit, not before.
- Where does the data live?
- On infrastructure you own, under UK data residency. Self-hosted databases (Postgres, SurrealDB) on a UK VPS by default. The bureau won't quietly route your customer data through someone else's analytics SaaS. If you have an existing data warehouse (Snowflake, BigQuery), the build connects to that without trying to replace it.
- How does an LLM-augmented workflow actually work?
- Take a real example: a recruitment firm receives 200 CVs a week, each one needs to be classified, key fields extracted, and matched to open roles. An LLM-augmented workflow ingests the inbox, runs each CV through a model with a structured-output prompt, writes the extracted data into the CRM, and flags edge cases for a human. The human stays in the loop on the decisions that matter; the LLM handles the rekeying. Built fixed-fee, runs on infrastructure you own, no per-CV API fee surprises.
- Will this hallucinate?
- LLMs will produce wrong output occasionally. The build accepts this and designs around it: structured-output schemas, human-in-the-loop on anything load-bearing, validation against the source document, and observability so you can see what the model did and why. The bureau treats LLM output the way a careful engineer treats user input: never trusted, always validated.
- How much does Data & AI cost?
- Scoped per engagement, usually slotted under an Engineering or Strategy build. A simple Metabase dashboard on top of existing data: typically £2,000 to £4,000. A multi-source pipeline with monitoring: £5,000 to £15,000. An LLM-augmented internal workflow with human-in-the-loop UI: £4,000 to £12,000. Fixed-fee, agreed before work starts.
Ready to put
your data to work?
Start with the free triage. Describe the question you can’t answer without a spreadsheet, or the document-shaped job that’s eating hours, and you’ll get an honest read on whether a Data & AI build is the right move.