The pipeline between your data and the decision.
Better foundations. Better evidence. Better decisions. We build the data platforms that make your information usable, and the AI systems that turn it into answers your teams can act on.
What we do
Most organisations have both problems at once.
A warehouse nobody quite trusts, and a document estate nobody can query. The two are usually owned by different teams, funded from different budgets, and failing for the same reason.
We build both paths, and they share their first five stages. Sources are surveyed, ingested, parsed, modelled and tested until the numbers can be relied on. From there the work forks, to a semantic layer your analysts can query, or to retrieval that grounds an answer in a citable passage. Both are measured against a set your team owns.
Service 01
Data & platform engineering
Warehouse and lakehouse build, migration, ingestion, modelling, and the contracts that keep the numbers trustworthy enough to act on.
See how it worksService 02
Intelligence extraction
Retrieval, extraction and enrichment over the documents, tickets, contracts and transcripts that never reached the warehouse.
See how it worksService 03
Data & AI strategy
A sequenced plan grounded in what your data can actually support, with the disqualifying constraints found before the budget is committed.
See how it worksService 04
Evaluation & assurance
Evaluation harnesses, adversarial testing and data contracts, so both the answers and the numbers underneath them can be checked.
See how it works
The mechanism
One spine, two branches, and the parts that usually break.
Both practices share the first five stages, which is why we sell them together. Every stage can be evaluated in isolation, and that matters when something degrades and you need to know whether a contract broke, retrieval got worse, or the model changed underneath you.
Why teams choose us
Built on mechanism, not adjectives.
No product to sell you.
Every recommendation is about your estate, because we have nothing of our own to place in it. When the honest answer is that a use case is not viable, that is the answer you get, and you get it early.
Evaluation from day one.
The labelled set, the regression suite and the assurance pack are built alongside the pipeline rather than bolted on afterwards. Nothing reaches your users without a measured baseline behind it.
Permissions inside the index.
Access is resolved before ranking, against your own directory, on every query. Filtering results after the fact is not access control. It is a leak with a user interface.
Built to be handed over.
Your engineers are in the repository from the first increment, and the engagement finishes when your team has changed the pipeline themselves. If you need us permanently, the handover failed.
By the numbers
VectisFlow at a glance.
Pipelines in production across both practices, the columns held under a data contract, and the answer-supported rate we hold ourselves to on every engagement.
Pipelines in production.
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Across water, government, financial and professional services.
Columns under contract.
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Shape, range and freshness expectations checked on every load, breaches blocking it.
Median answer-supported rate.
0%
Measured on held-out sets, per document family, gated in CI.
Where we build
Your platform, not ours.
We have no product to sell you, which means we have no reason to recommend one. Engagements are built on the platforms you already run, including Microsoft Azure and AWS, whose marks we do not display because their brand guidelines require assets to be sourced and approved directly. Vendor marks below indicate platforms we build on, not partnership or endorsement.
How we work- DatabricksLakehouse and batch processing
- SnowflakeWarehouse and derived tables
- PostgreSQLRelational store and pgvector index
- Apache AirflowPipeline orchestration
- Apache SparkDistributed transformation and parsing
- OpenSearchLexical retrieval and hybrid ranking
- QdrantVector index at scale
- DuckDBLocal analysis and evaluation runs
- PythonPipeline and evaluation code
- Hugging FaceModel hosting and embeddings
- MLflowExperiment and model tracking
- OpenTelemetryTracing across pipeline stages
- GrafanaPipeline and quality dashboards
- KubernetesService deployment
- TerraformInfrastructure as code
- RedisCaching and queues
Engagements
What the work looks like, end to end.
Six engagements written the way the work actually runs: the estate, the constraint, the pipeline stage by stage, and what shipped.
Water & utilities
Making thirty years of asset documentation answerable at a UK water utility
A retrieval pipeline over 380,000 asset documents: drawings, condition reports, permits and handover packs, with permission inheritance preserved from the source document management system.
UK regional water utility, ~4,500 staff · 16 weeks
Read the engagement
Central government
Assembling regulatory evidence from casework correspondence in central government
An extraction and evidence-assembly pipeline over eleven years of casework correspondence, built to a standard where every extracted fact traces to a source passage and a pipeline version.
UK central government agency, ~2,000 staff · 20 weeks
Read the engagement
Financial services
Putting a measured error rate behind claims-evidence retrieval at a specialist insurer
An evaluation harness, adversarial suite and assurance pack retrofitted to a retrieval system already in production, so the firm could state its error rate rather than describe its intentions.
UK specialist insurer, ~1,200 staff · 12 weeks
Read the engagement
Sectors
Where we have depth.
Three sectors where both estates are large, the permission model is real, and the cost of a wrong answer is high enough that evaluation is not optional.
Water and utilities
Regulated network operators run meter, SCADA and job data alongside thirty years of documentary history, and most operational questions need an answer drawn from both.
Water and utilities
Central government
Departments hold decision-relevant material in correspondence and in the transactional systems beneath it, under explainability and residency constraints that rule out most of the market.
Central government
Financial services
Regulated firms can build pipelines and retrieval systems quickly, and can deploy neither without evidence. Assurance is the constraint, and it is the one most projects under-resource.
Financial services
“A contract that cannot stop a load is not a contract.” “Chunking is a decision, not a default.” Notes from the build, on the decisions in both practices that decide whether a system survives its first quarter.
Read the insightsGet in touch
Talk to us.
A first conversation runs about forty-five minutes and covers three things: what your estate actually looks like, whether anyone can define a correct answer or an agreed number, and whether your permission model resolves per user. Any one of them can rule the work out, and we would rather tell you in week one.
- Prefer email
- hello@vectisflow.com
- Response time
- One working day, from a person who has read it.