Connect, organise, improve and use your information
For businesses with plenty of information they cannot use effectively. Whether the answer is a modern data platform, better architecture, new integrations or an Answerable deployment, we start from what you already run and change only what the work needs, with or without AI.
The problem
“Our business has plenty of information, but we cannot use it effectively”
The information is there, spread across documents, databases, cloud storage and business applications, yet people cannot find what they need or rely on what they find. The cost shows up as manual rework and projects that stall.
None of this needs AI to be worth fixing. Reliable, well-understood data pays off in reporting, operations and integration on its own, and it is also what any later AI work depends on.
Who it is for
- Organisations at any stage with AI, including those that have barely started and those with no AI plans at all
- Businesses that rely on information spread across documents, databases, cloud storage and business applications
- Teams whose reporting, integrations or platform have not kept up with how the business now works
- Programmes where analytics or AI pilots have stalled for lack of usable data
Who we work with
- The CIO, CTO or chief data and analytics officer who owns the systems and the data
- The business leader who owns the outcome, for example a finance, operations, commercial or research director
Usually both together, alongside your own data, engineering and application teams, who run the capability after handover.
What we do
Six stages of data work, run as a cycle
Each stage is useful on its own, and you may already have some in place, so we start where your information is today. Choose a stage to see what happens, what you receive and, if you want it, the technical detail.
The stages form a cycle: Keep improving leads back to Connect as sources and needs change. Many organisations already have some stages in place, so the work starts wherever yours is.
Bring authorised sources together
Connect
We connect the documents, databases, cloud storage and business applications you have authorised us to use, taking care not to disrupt the systems people rely on. Connecting a source makes it reachable, and its accuracy is assessed and improved in the stages that follow.
What you receive
An agreed inventory of connected sources, each with a named owner and a documented, secure route in.
Technical detail
- Connections to document stores, cloud object storage, relational databases and enterprise application APIs
- Change data capture or scheduled extracts from source systems, chosen source by source
- Least-privilege service identities, with read-only access agreed with each system owner
- Data landed in your own environment with its source and extraction time recorded, or queried in place where copying is not appropriate
Find out what you have and what it means
Understand
We find out what each source holds, how it is structured and related to the others, what state it is in, which business terms people use for it and who is allowed to see it. Decisions about what to change can then rest on evidence about your own data.
What you receive
A written profile of every source, including measured quality and known gaps, plus a glossary of the business terms that matter.
Technical detail
- Schema and lineage profiling across sources
- Data quality profiling for completeness, validity, consistency and duplication, measured against agreed rules
- Key and relationship discovery between systems, including where the same customer or product is held more than once
- A business glossary agreed with the people who use the terms
- Permission and sensitivity mapping, including personal data under UK GDPR
Organise, clean and enrich it
Prepare
We extract the information the work needs, then organise, standardise, classify, deduplicate, enrich and check it. Quality improves because it is worked on deliberately and measured, with problems fixed at the source wherever possible.
What you receive
Curated, documented datasets and document collections that pass agreed validation checks, with the rules written down so your team can run them.
Technical detail
- Text and table extraction from documents, including optical character recognition for scanned files
- Normalisation to agreed formats, units, codes and reference data
- Entity resolution and deduplication using match rules you can inspect
- Classification and metadata enrichment, checked by sampling
- Validation rules and data contracts that stop failing records moving downstream
- Layered storage on your platform, for example a Databricks or Microsoft Fabric lakehouse, keeping raw, cleaned and curated data apart
Make it findable and safely queryable
Make accessible
We make each kind of information available in the way that suits it. Documents become searchable by keyword and by meaning, while figures such as sales, margin or availability are queried from governed sources using agreed definitions, so everyone gets the same number.
What you receive
Search and query access that respects permissions, and one agreed set of definitions for the measures that matter.
Technical detail
- Keyword and vector indexes over unstructured text, with embeddings created as documents are indexed
- Hybrid keyword and vector retrieval with semantic reranking at query time
- Governed SQL access through a semantic layer for structured measures, against the source or a warehouse
- Business semantic definitions of measures such as margin or availability, owned by the business and versioned
- Figures calculated deterministically in SQL or code, with language models used only to interpret and explain them
- Source permissions carried through, so search results and queries respect who may see what
Use it in decisions and daily work
Put to work
We put the information to use in the work itself: questions answered with the evidence attached, analytics, AI agents, business workflows and approved changes to operational systems. AI is used only where it earns its place, inside agreed permissions and with a named person accountable.
What you receive
Working tools in the hands of the people who need them, each with a named owner and a baseline to measure against.
Technical detail
- Retrieval-augmented answers that cite their sources, tested against an agreed set of questions
- Analytics and reporting built on the governed semantic layer
- AI agents with scoped tool permissions and human approval before any operational change
- Workflow integration through APIs, events or the business applications people already use
- Audit trails of the questions asked, the sources used and the actions approved
Keep it current and prove its value
Keep improving
Sources and business needs keep changing once a capability is in use, so the work continues. We keep sources synchronised, monitor quality and access rights, evaluate answers against agreed tests and measure value against the baseline, and what we learn feeds the next round of the cycle.
What you receive
Regular reports on quality, usage and value against the baseline, and a backlog of improvements in priority order.
Technical detail
- Incremental synchronisation with change detection and freshness monitoring
- Data quality monitoring and alerts against the agreed rules
- Rights management: access reviews, retention and removal of withdrawn content
- Answer evaluation against a maintained test set, covering retrieval quality and citations
- Usage, cost and value measured against the original baseline
For architects and CIOs
Engineering choices we hold to
Figures stay structured
Financial and transactional data is queried where it lives, in the source or a warehouse, through governed access and agreed definitions of measures such as margin or availability, and figures are calculated deterministically. Embeddings and vector search are for documents and other unstructured text.
Quality is worked on deliberately
Connecting a source makes it reachable. Its accuracy is measured against agreed rules and improved on purpose, at the source wherever possible, and the rules stay with your team.
Relevance is ranked when a question is asked
For document search we combine keyword and vector retrieval, and semantic reranking orders the results at query time, as part of retrieval.
Your platforms first
We build on the platforms you already run when they suit the work, and recommend replacing one only where that is the better choice.
How to begin
Five steps, starting with Discovery
Data modernisation has no separate price. It starts with Discovery. Each step runs through a service we already offer, at its published starting price. All prices exclude VAT, and each later step is scoped and priced in writing before it starts.
Step 1
We assess your existing information estate
Your sources, systems, integrations and reports: what they hold, how people use them and where they let the business down.
You receive: Findings on what you have, the state it is in and what is holding the work back.
Delivered through
- Discovery from £7,500
- Independent Review from £7,500When the question is a second opinion on an existing plan or platform
Step 2
We recommend the minimum appropriate improvements
Only the changes the work needs, in priority order, with options and costs. That might be a modern data platform, better architecture, new integrations or a few sources put right, and the recommendation can be to change nothing yet.
You receive: A written recommendation and a costed first phase, which you can take to us, to your own team or to another provider.
Delivered within the same Discovery or Independent Review as step 1.
Step 3
We engineer and integrate those changes
Pipelines, platforms, integrations and data models, built in working increments against acceptance criteria agreed up front, on the platforms you already run wherever they suit the work.
You receive: Working data foundations, with documentation, handover and source access agreed in the contract.
Delivered through
- Build & Productionise from £15,000
- Fix & Optimise from £10,000When an existing programme is stuck
Step 4
We make the information useful
Through the tools you already have, through Answerable, or through another product that suits the job, whether that is reporting, search, analytics, an agent or a workflow.
You receive: People using the information in their work, measured against the baseline agreed at the start.
No separate price: this is part of the build. Any Answerable licence is agreed separately from consultancy fees.
Step 5
We support and improve the capability once it is in use
Synchronisation, quality monitoring, evaluation and improvement on an agreed monthly remit, with your team able to change or stop the work.
You receive: A capability that stays current, with regular reports on its quality and value.
Delivered through
Putting information to work
Answerable is optional
Answerable is one way to put information to work, alongside the tools you already have and other products. It is TechGuidr’s own product: it combines keyword and vector search over your documents and writes answers that cite each source with a numbered footnote.
If it suits the work, we will say so, disclose our interest and compare it with the alternatives. Any Answerable licence is agreed separately from consultancy fees.
Ways to put information to work
- The reporting, analytics and business applications you already use
- Answerable, TechGuidr’s own product, for questions answered from your documents with each source cited
- Another product that suits the job, chosen on its merits
Why trust us
Where our data experience comes from
Dan Warrener founded TechGuidr after more than 25 years in technology, including enterprise data warehouses, business intelligence and cloud data platforms. Most of the data platform work here was done before TechGuidr, and each item says where it was done and what kind of evidence stands behind it.
A SaaS platform’s data rebuilt
Re-architected the data platform behind a learning and wellbeing business on an Azure Databricks lakehouse, with verified calculation pipelines and a structured dimensional model. Dashboard queries that had taken four to five seconds returned in under 600 milliseconds once unnecessary middleware was removed.
Evidence: Measured (query times, as reported in the case study)
Read the case studyFinding where the stock was going
For a leading UK retailer, designed and built a data warehouse platform recording the patterns in stock movement, with automated analysis around the events where losses clustered.
Evidence: Qualitative
Tracking every donated bag
For a national charity, designed and built a greenfield Azure data platform, connected legacy tills for real-time sales and introduced IoT tracking across vehicles, textile banks and clothing bags.
Evidence: Qualitative
A global engine fleet, seen in flight
Helped build and deliver a cloud data platform analysing petabytes of engine data for patterns and anomalies, on Microsoft Azure and Power BI.
Evidence: Qualitative
Analytics and data leadership
Centralised AAA games studio analytics in the early days of Azure and migrated global BI workloads from on-premises to Azure. Later Global Data & AI Lead for Microsoft’s Retail & Consumer Goods Solutions Team, 2022 to 2025.
Evidence: Qualitative (role history)
Retrieval engineering we built ourselves
Dan conceived, designed and built Answerable, including how it indexes documents for combined keyword and vector search and cites its sources.
Evidence: Qualitative (what was built)
Before you commit
Common questions
Do we need Answerable?
No. Answerable is TechGuidr’s own product and one way to put information to work, alongside the tools you already have and other products. Most of the value of data modernisation comes from connecting, understanding and preparing information, which helps the reporting, analytics and applications you already use. If Answerable suits the work we will say so and disclose our interest, and any licence is agreed separately from consultancy fees.
Will you replace our existing platform?
Only where replacing it is the better choice. When the platforms you already run suit the work, we build on them. Discovery sets out the options and their trade-offs before anything changes.
Do you put our financial data into a vector database?
No. Financial and transactional data stays structured. It is queried through governed access to the source or a warehouse, using agreed definitions of measures such as margin, and figures are calculated deterministically. Embeddings and vector search are for documents and other unstructured text.
How long does it take?
Discovery, the first step, typically takes one to three weeks for a fixed scope, and larger or multi-system environments are scoped separately. What follows depends on what Discovery recommends: build work is scoped in phases and delivered in working increments, with the scope and price agreed in writing before it starts.
What does it cost?
Data modernisation has no separate price. It starts with Discovery, from £7,500, or with an Independent Review, from £7,500, when you want a second opinion on an existing plan or platform. Build & Productionise starts from £15,000, Fix & Optimise from £10,000 and Managed Improvement from £5,000 a month. All prices exclude VAT.
We have barely started with AI. Is this for us?
Yes. This is often the right place to start, because well-organised, trusted information pays off in reporting and operations whether or not AI follows.
Tell us where your information lets you down
Two or three sentences on your systems and what you cannot do today is enough to start. Data modernisation begins with Discovery, from £7,500 ex VAT.