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Case study · Life sciences · Generative AI
AI Conversational Analytics Assistant for Diagnostics Sales Teams
A global in-vitro diagnostics manufacturer needed a conversational analytics assistant because its commercial teams waited on analysts for every answer. Brainy Neurals built an assistant that uses large language models to turn plain-English questions into queries on the governed data warehouse. Marketing & commercial staff type into a secure chat, while analysts check the work in a second view that replaced hand-built SQL reports. Questions that once took days now come back in seconds, each one shown with the exact query behind it.
In seconds
Time to a data answer
Every answer
Ships with its query
Plain English
How the teams ask
Published October 2026
At a glance
The engagement with a global diagnostics manufacturer fits into four short answers & one table of facts about the client & the build.
What problem did this solve?
Marketing & commercial teams at the client couldn’t get go-to-market data answers until an analyst wrote the query. Each follow-up meant another wait for an analyst to come free.
What did Brainy Neurals build?
Brainy Neurals built a plain-English analytics assistant, grounded in generative AI, for a global diagnostics manufacturer. Business users ask in their own words & get an answer with a table & a chart from the data warehouse.
What changed after it went live?
Common go-to-market questions now come back in seconds, with no analyst in the loop. Every answer shows the query behind it, & proven queries get saved for reuse.
Who else could use this?
Any organization with a data warehouse & business teams who can’t query it directly could use the same pattern. Marketing, sales, finance & operations teams all qualify.
Engagement facts
| Detail | Value |
|---|---|
| Industry | Life sciences & diagnostics |
| Client type | Global in-vitro diagnostics manufacturer |
| Engagement | Conversational analytics platform |
| Timeline | Not disclosed |
| Capabilities | Generative AI & data analytics |
| Delivery model | Project-based delivery |
Why couldn’t teams answer their own questions?
Teams at a global in-vitro diagnostics manufacturer couldn’t answer their own questions, because nothing let them ask the data warehouse directly. The client runs its commercial strategy on data spread across several systems & wanted a conversational analytics assistant to close the gap.
A business user couldn’t query the warehouse the way an enterprise chatbot answers a question. Every request went to an analyst, who wrote the SQL (the query language a database reads) by hand & sent back a static report.
- A queue for every question. Each new question meant filing a request & waiting for an analyst.
- No reuse at all. Repeat questions still needed a fresh query & a hand-built chart every time.
- No room for a hunch. Every follow-up went back into the same queue as the question before it.
- Answers that didn’t match. Two analysts could answer one question two ways, so the format changed with whoever built it.
Letting people query data in their own words has been a research goal since the 1970s[1]. The goal stays hard because a warehouse stores machine terms, while people ask in business ones.
Why do the obvious fixes stop short?
The obvious fixes for slow data answers each get part of the job right, yet three of the four stop at the same wall.
| Approach | What works | Where it fails | Still suits |
|---|---|---|---|
| Ad-hoc analyst requests | Accurate, governed answers | Slow, analysts can’t scale | Rare one-off questions |
| Self-service BI dashboards | Fast for known questions | Can’t answer new questions | Stable recurring metrics |
| A generic AI chatbot | Understands plain language | No governed data, invents answers | General knowledge only |
| Grounded assistant, our route | New questions, checkable answers | Needs setup & governance first | Trusted ad-hoc answers |
Turning a plain-English question into correct SQL is still an open research problem[2], which is why the fourth route needs careful engineering.
The conversational analytics assistant we built
Brainy Neurals put a plain-English assistant in front of the client’s governed data warehouse, so a user can ask a question in everyday words.
The analysis agent, built by our AI agent development team on large language models, plans each analysis & runs the queries. Then it reasons over the rows that come back & returns an answer with a table & a chart.
The warehouse stays the single source of truth, & access rules decide what each person can see. Mitesh Patel, our AI architect, set this architecture & reviewed every decision on how the assistant reaches an answer.
Everything that touches a question sits inside one governed platform, & the warehouse stays the source.
Show the work
The first decision was to ship every answer with the query & reasoning that produced it. We rejected a black-box answer that returns only a number. An unexplained number in a regulated business gets ignored or double-checked, which erases the time saved.
An unexplained number in a regulated business gets ignored or double-checked, which erases the time saved.
Two views over one engine
The second decision was to build two views over one engine. Business users get a clean chat that answers the question. Analysts get a technical view of the same conversations, where they check results & save proven queries.
We rejected one shared screen, because business users want it simple while analysts want depth.
Which technology stack runs the assistant?
The stack behind the assistant has three layers. Each layer had to earn its place, because a governed analytics tool lives or dies on trust.
The same retrieval layer runs in our knowledge base work over a different set of documents. We keep model names off this page on purpose.
Data & access
A governed data warehouse is the single source of truth for every answer. Single sign-on with role checks lets only cleared users in.
AI & reasoning
An analysis agent plans multi-step queries, & large language models read intent & write the answers. A saved-query search finds proven answers to reuse.
Application & delivery
A Python service layer holds the business logic behind a chat view & an analyst view. A document store keeps history with PDF & HTML export, & token & cost tracking by model ran from day one.
We ruled out scattered spreadsheets, open access, hand-written SQL & rules-only parsing. Logic living in the interface & one shared view for both audiences were off the list too, along with rebuilt queries & a setup that kept no history or cost data.
How does one question become an answer?
One question moves from the chat box to a finished answer in six steps, & none of them leave the governed platform.
- A user types a go-to-market question into the secure chat in plain English, the way they’d ask a colleague.
- The assistant checks the saved queries first & reuses a proven one when a similar question already exists.
- The agent plans the analysis & runs its queries against the governed warehouse, never against a copy.
- It reasons over the returned rows & applies the filters & groupings the question asks for.
- It builds the answer as a short written summary with a data table & the chart that fits the data.
- It returns everything with the query & reasoning, so the user can trust the answer or check it.
What broke before anyone trusted it?
Three things broke in the field before anyone trusted the assistant. Each fix was simple to describe & slow to find.
Words meant different things
Before the fix, terms like performance & region read differently across teams, so the assistant gave confident answers to the wrong question.
Now every answer names its assumptions, so a wrong reading gets corrected instead of trusted.
Numbers arrived without proof
At first, a number with no query behind it got double-checked anyway, which erased the time saved.
Now the exact query & reasoning ship with every answer, & analysts verify & sign off.
Cost stayed invisible
Early on, nobody could see which questions were expensive or how fast usage was climbing.
Now token usage is tracked by model, so expensive patterns show up early.
Real questions carry ambiguity by nature, from overlapping names to more than one path through the data[3]. These are the weeks when specialist engineers earn their place instead of learning it slowly.
An AI proof of concept is where this work belongs, before anything gets promised. The saved-query idea, by the way, is an old librarian’s trick applied to SQL.
What changed after it went live?
After go-live, a business user asks in plain English & gets the answer in seconds, where a data answer once meant filing a request & waiting. Users write no SQL at all, & every answer shows its query.
| What changed | Before | Now |
|---|---|---|
| Getting a data answer | File a request & wait | Ask in plain English, seconds |
| A follow-up question | Another request, another wait | Just ask the next question |
| Who can explore data | Analysts only, on request | Any authorized business user |
| A proven query | Rebuilt from scratch each time | Saved, searched & reused |
| The output | Varied by whoever built it | Consistent answer, table & chart |
We haven’t published a time-saved figure. The client reports that questions which once took days now come back in seconds, & a number we haven’t measured is a number we won’t print.
The conversational analytics assistant runs in production for the client’s marketing & commercial teams in the life sciences business. Analysts verify results in the technical view & save proven queries for everyone else. One governed source feeds every number on screen.
Day to day, a marketer can follow one question across three or four turns in a single sitting, & the pattern has started to spread to other functions.
Ask the question your dashboards cannot answer
Tell us about your warehouse & the questions your teams keep waiting on. Or check first whether your data is ready for a plain-English analytics layer.
What would we do differently?
Four lessons came out of the build, & each one cost us something to learn.
Define the metrics first
Avoid choosing a model while terms like performance & region stay fuzzy, because we shipped answers that were right & useless.
Repeat what worked later, which was pinning down the business metrics before any model work.
Build trust in from day one
Avoid treating the reasoning trail & the shown query as extras, a mistake we made at first.
Repeat the day-one trust features, since they’re why people use the answers at all.
Track cost from query one
Avoid adding usage tracking late, because retrofitting visibility is harder than starting with it.
Repeat the habit of metering every query from the very first one.
Keep the analyst inside
Avoid a design that sidelines the human analyst, since it breeds skeptics instead of owners.
Repeat the technical view, which turned skeptics into owners who could verify the work.
Where else does this pattern fit?
A conversational analytics assistant fits wherever business teams wait on analysts for answers from a governed data warehouse.
Porting it needs a connection to the new warehouse & that team’s business terms. Access rules then get matched to the data.
AI in retail
Merchandisers ask about sell-through without waiting on an analyst. The build swaps in a retail data model with promotion & store dimensions.
AI in banking & finance
Product teams ask about portfolio & customer trends in plain English. Access gets tighter, with an audit trail on every query.
AI in manufacturing
Operations teams ask about output & quality by line. Plant & ERP data replace the commercial warehouse, along with shop-floor terms.
AI in logistics
Planners ask about shipments & capacity by region. Warehouse & transport systems plug in, with time-window logic.
Healthcare payer teams
Payer teams ask about utilization & access in plain English. Strict de-identification applies, & access matches how sensitive the data is.
Questions buyers ask about conversational analytics
Can business users query a data warehouse without knowing SQL?
Yes, business users can query a data warehouse without SQL when a conversational layer sits in between. The assistant maps the plain-English question to the governed data & writes the query itself, so the person asking never touches SQL.
How is conversational analytics different from a BI dashboard?
A BI dashboard answers only the questions it was built for, while a conversational analytics assistant handles new ones in plain language. It returns each answer with the query behind it.
How does the assistant make sure the answer is correct?
The assistant shows its full working on every answer, with the exact query & the reasoning it used. Analysts can verify it, & the governed warehouse stays the single source of truth.
How long does it take to build a conversational analytics assistant?
A proof of concept on your own warehouse usually takes a few weeks. Access rules & business terms each need a pass, along with the hardest questions. A production rollout follows once the answers hold up, as it did for this life sciences client.
How much does conversational analytics cost to build?
The cost depends on your warehouse & the questions in scope, plus what already exists. Brainy Neurals scopes it from a short conversation & a look at your data, then quotes a fixed price. An AI readiness assessment shows first whether your data is ready.
Is our data secure, & who can see what?
Yes, access runs through single sign-on with role-based rules, so people see only what they’re cleared for. Every question gets logged, & the assistant queries the governed warehouse, never a copy.
What do your teams keep asking the data?
Send the question your marketing or commercial team keeps waiting on. Mitesh Patel reads every message & replies within one working day with a view on whether your warehouse can answer it.
Services behind this case study
Six Brainy Neurals services carried this build from scoping to production.
Generative AI development
Enterprise chatbots & assistants that answer in plain language & cite the data behind them.
AI agent development
Agents that plan a task & run the steps, with a reasoning trail you can check.
RAG development services
Retrieval that finds proven queries & grounded context, so answers stay tied to real data.
AI consulting & strategy
Advice on where to start & which data to trust before a plain-English layer goes in.
Hire AI developers
Specialist engineers who extend your team for the weeks the data fights back.
AI in healthcare
Analytics & document AI for life sciences teams working under real governance.
An AI proof of concept is the fastest way to test the idea on your own warehouse, & our engagement models show how a project like this runs. An AI readiness assessment checks whether your data can carry it, & the industries hub shows where the pattern already works.
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Cite this case study
Shah, Rushabh & Patel, Mitesh. AI Conversational Analytics Assistant for Diagnostics Sales Teams. Brainy Neurals, September 2026. https://brainyneurals.com/case-studies/conversational-analytics-assistant/
Sources cited on this page
- Koutrika G. Natural Language Data Interfaces: A Data Access Odyssey. 27th International Conference on Database Theory, ICDT 2024. LIPIcs vol 290, pages 1:1 to 1:22. DOI 10.4230/LIPIcs.ICDT.2024.1
- Katsogiannis-Meimarakis G and Koutrika G. A survey on deep learning approaches for text-to-SQL. The VLDB Journal, 2023, 32(4), pages 905 to 936. DOI 10.1007/s00778-022-00776-8
- Bhaskar A, Tomar T, Sathe A and Sarawagi S. Benchmarking and Improving Text-to-SQL Generation under Ambiguity. Findings of the ACL, EMNLP 2023. arXiv 2310.13659