The models and taxonomies the rest of the stack depends on
We design engagement scores with visible weights, ontologies that keep tagging consistent, extraction pipelines with review queues, and the evaluation gates that decide what ships.
A week in data and analytics, before and after
- Mon 9:00A health score exists but nobody can say what moves it.The scoring spec lists inputs and weights, and the review cadence for changing them.
- Tue 11:00Three teams tag the same concepts three different ways.One ontology defines the entities and relationships, and each pipeline uses it.
- Wed 14:00An extraction pipeline writes straight into the warehouse and errors are found weeks later.Extracted records pass through a review queue with confidence shown before they land.
- Thu 10:00Model choice is whichever was used last time.Models are chosen per step on measured quality and cost against your own tasks.
- Fri 13:00Nobody can say whether the AI output got better or worse this month.The evaluation harness reports a scorecard, and a quality gate blocks regressions.
What we build for data and analytics teams
6 capabilities, each with its own page: the deliverables, the steps, and what is not promised.
User engagement scoring
A transparent engagement or health score built on the behaviours that predicted retention in your own data, with the weights written down.
See the deliverablesKnowledge graph and ontology design
The entity types, relationships and confidence rules that let a company's knowledge be connected instead of piled.
See the deliverablesAI output evaluation and quality gates
Frozen scorers and checks that decide whether an AI output is good enough to ship, so quality is measured rather than eyeballed.
See the deliverablesDocument and intake extraction
PDFs, forms, emails and transcripts turned into structured records — names, dates, amounts, decisions — queued for a person to approve before they land anywhere.
See the deliverablesPII protection and data handling
Redaction, minimisation and handling rules for personal data in transcripts, tickets and documents before any model sees them, with residency where your obligations require it.
See the deliverablesModel selection and cost optimization
Which model, at which size, for which step — chosen by measured quality and cost on your own tasks rather than by the leaderboard.
See the deliverablesWhat stays under your control
The promises that hold on every engagement, and the ones we do not make on this one.
On every engagement
- Outputs cite the document or conversation they came from, so a person can check before acting.
- A person approves anything that reaches a customer or writes to a system of record.
- Delivery is Slack, email or a document you own. Other channels are not offered.
- Fixed price, agreed before we start. Nothing numeric is published on this site.
The work runs on the TurnkeyGTM platform where it fits, and on plain software where it does not.
Not promised here
- Designs and first builds are the deliverable; ongoing operation moves to your team with documentation.
- Figures on quality and cost are per engagement and never published.
- We do not replace your warehouse or BI tool.
How an engagement runs
One call to scope it, one fixed price to approve, one build you can trust before it goes live, then a handover.
The full process- 1
Scope the work
A thirty-minute call on what your team does by hand every week, where the information lives, and who needs the output.
- 2
Fixed price, ROI up front
You get a written proposal with a fixed price and an honest estimate of the hours it gives back.
- 3
Build beside your process
We design and build the automation and run it next to the manual version until the outputs match and you trust them.
- 4
Hand over, keep it running
Documentation and runbooks so your team owns it.
Questions we get asked
Teams data and analytics works with
Product & research: Product, UX research and data: the teams that decide what gets built and whether it worked.
Revenue operations
Connect the record across CRM, calls and tickets so a figure in a report can be traced to the record it came from.
Learn moreGrowth marketing
Find the accounts and behaviours worth an experiment, then keep the results where the next experiment can reuse them.
Learn moreSoftware engineering
Put AI to work on the parts of engineering that eat the week, with evaluation gates so the output earns its place in the codebase.
Learn moreTell us what your data and analytics team does by hand.
A thirty-minute call is enough to know whether it is worth automating. Fixed price, agreed before we start.