Consulting
Data and AI consulting for when the data is what holds your AI back
When AI stalls, check the data underneath it first. Kriv AI gets the data in order, on Databricks or Microsoft Fabric, then builds the AI that depends on it, with governance your reviewers can follow.
Data and AI consulting is help with both halves of an AI project: getting the data ready, governed and accessible, and then building the AI that uses it. It suits organisations whose AI pilots stall because data is scattered, untrusted or locked away. Kriv AI starts with an AI data strategy, builds on Databricks or Microsoft Fabric, and adds AI on top with controls a reviewer can follow.
what it is
What data and AI consulting covers
AI consulting on its own assumes the data is ready. In regulated organisations that is a big assumption: customer records sit in several systems, documents live in shared drives with years of loose permissions, and nobody can say which copy of a figure is the right one. An AI system built on that foundation produces answers nobody trusts.
Data and AI consulting treats the two as one job. We find out what data the AI needs, get it into a governed platform, decide who may use it, and only then build the AI. If your data is already in good shape and you need help choosing and building AI use cases, our AI consulting services page is the better starting point.
signs
Signs the data is what is holding your AI back
A pilot worked on a clean sample but fails on real records. Different teams get different answers to the same question because they draw on different copies of the data. An AI assistant cannot be connected to the documents people need because nobody is sure who may see them. Reviewers block a launch because nobody can show what data the AI used.
Each of these is a data problem, not a model problem, and a better model will not fix it. The fix is to decide which data each use case needs, give it an owner, put it somewhere governed and record how AI uses it. That is the work this page describes.
data strategy
An AI data strategy before anything is built
An AI data strategy answers five questions for each AI use case you plan. We work through them with your data and business owners.
1. What data does the use case need?
The specific tables, documents and records, not 'all our data'.
2. Where does it live today, and who owns it?
Each source, its owner and how current and correct it is.
3. May AI use it?
Sensitivity, contractual limits and the policy your organisation already applies.
4. What has to change first?
Consolidation, cleaning, access fixes or lineage that must exist before AI reads it.
5. How will we show what the AI was working from?
The lineage and access records a reviewer will ask for.
services
What a data and AI consultant from Kriv AI does
AI and ML consulting on governed data
Where a use case needs a trained model rather than an assistant, we take notebook code into tested production jobs and serve models under an approval process on the platform your data lives on. We do not yet have a named, published case study for this work, so we scope from your data and your use case rather than from a slide.
| Stage | What we do | What you get |
|---|---|---|
| Assess | Map the data each AI use case needs, its owners, quality and access. | An AI data strategy with the gaps ranked. |
| Platform | Organise the data on Databricks or Microsoft Fabric with clear layers and governance. | A governed platform your team can run. |
| Govern | Set access, classification, lineage and retention for data AI will use. | Rules and records your reviewers can check. |
| Build | Build the AI on the governed data: assistants, retrieval, agents or models. | AI with an owner, a review step and logs. |
| Hand over | Document, train your team and transfer ownership. | Something your own staff maintain. |
platforms
Platforms: Databricks and Microsoft Fabric
We build on the platform that fits your estate. Kriv AI is a Databricks partner, and our Databricks consulting page covers lakehouse builds with governed access and lineage. Kriv AI is also a Microsoft Solutions Partner for Cloud & AI Platforms · Partner ID 7036589, and our Microsoft Fabric consulting page covers Fabric and OneLake work for Microsoft organisations.
We are platform-pragmatic: if your data already lives well on one of them, we build there rather than proposing a migration.
regulated
Data and AI consulting for regulated industries
Our focus includes healthcare, life sciences, financial services and insurance, where the data AI reads carries legal and contractual obligations and every output may be questioned. We write data and AI rules to the policy and frameworks you already answer to, and we keep records of what each AI system was working from. Your risk, privacy and compliance functions keep the final judgement. Our Databricks AI for financial services page shows the approach in a banking setting.
how an engagement runs
How a data and AI engagement runs
1. Discovery call
We learn your AI goals, your data estate and how your organisation reviews new systems. Work is scoped and priced after this short discovery call.
2. AI data strategy
We work through the five questions for your first use cases and rank the data gaps.
3. Platform and governance
We fix the gaps that block the first use case, on the platform you choose.
4. Build and hand over
We build the AI on the governed data, run it under review, and hand over with documentation and training.
Related resources
Continue exploring
Straight answers
Frequently asked questions about Data and AI consulting for when the data is what holds your AI back
What is data and AI consulting?
It is consulting that covers both the data an AI system depends on and the AI itself: assessing the data, organising and governing it on a platform such as Databricks or Microsoft Fabric, and then building AI on top with controls a reviewer can follow. It suits organisations whose AI efforts stall because the data is not ready.
What does a data and AI consultant do?
They map the data each AI use case needs, find the gaps in quality, ownership and access, fix the ones that block the first use case, and build the AI on the governed result. At Kriv AI, a senior practitioner who both scopes and delivers does this work, without handoffs between a strategy team and a delivery team.
What is an AI data strategy?
A short, use-case-led plan for the data your AI needs: what data, where it lives, who owns it, whether AI may use it, what must change first and how you will show what the AI was working from. It is narrower and more practical than a general data strategy, because it starts from specific AI use cases.
Do you offer AI and ML consulting?
Yes, where a use case needs a trained model. We take notebook code into tested production jobs and serve models under an approval process on the platform your data lives on. We do not yet have a named, published case study for this work, so we scope it from your data and use case.
Do we need to migrate to Databricks or Fabric?
Not necessarily. If your data already lives well on a platform, we build there. Where data is scattered across systems with no governed home, consolidating on Databricks or Microsoft Fabric is a sensible answer, and we recommend the one that fits your existing estate and skills.
Which comes first, the data or the AI?
The use case comes first, then the data it needs, then the AI. Starting from a specific use case keeps the data work small and focused: you fix the sources that use case depends on, not the whole estate. Each further use case reuses the governed data and adds only what it needs.
How is this different from AI strategy consulting?
AI strategy consulting decides where AI should help the business. Data and AI consulting makes a chosen use case possible by getting its data ready and building the AI on it. You may need both: strategy to choose the use cases, then data and AI work to deliver them.
How much does data and AI consulting cost?
Our work is scoped and priced after a short discovery call, once we know your first use cases, where your data lives and what your reviewers require. Platform licensing is separate and set by each vendor.
Talk to the team that would do the work
Bring your requirements to a working session with the person who'll actually deliver.
Book a Discovery Call