Enterprise AI Assistant for Sourced Answers in Life Sciences

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Enterprise AI Assistant for Sourced Answers in Life Sciences

A large multinational molecular diagnostics enterprise needed an enterprise AI assistant, because its answers sat in systems most staff couldn’t query. Brainy Neurals built one that uses question routing & grounded search to answer from the right source with a citation. Teams across research, commercial & compliance ask it through one interface instead of sending tickets to analysts or IT. Staff now get sourced answers in seconds, & analysts no longer field routine lookups.

  • #EnterpriseAIAssistant
  • #ConversationalAI
  • #RetrievalAugmentedGeneration
  • #LifeSciences
  • #MolecularDiagnostics

Seconds

Time to a routine answer

One interface

Where every team asks

Every answer

Ships with its source

Rushabh

By Rushabh , Senior AI Research Engineer · Reviewed by Mitesh Patel, NVIDIA Certified AI Architect

Published October 2026

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At a glance

At a glance, this case covers one routing assistant built for a regulated diagnostics business, from the problem to the running system.

What problem did this solve?

Staff at a large molecular diagnostics enterprise couldn’t get quick answers. The information lived in separate document stores, a data warehouse, portals & the public web.

What did Brainy Neurals build?

Brainy Neurals built a routing assistant for a multinational diagnostics enterprise. It reads a plain-language question, picks the right tool & answers from the correct source with citations.

What changed after it went live?

Employees now ask one interface instead of routing requests through analysts or IT. Answers arrive in seconds, each with its source, so staff can audit any reply themselves.

Who else could use this?

Any organization whose answers sit in more than one system can use the same pattern. Finance, insurance, logistics & legal teams face the same scattered data problem.

Fact Detail
Industry Life sciences
Sub-vertical Molecular diagnostics
Client Multinational diagnostics enterprise
Engagement Enterprise AI assistant
Timeline Not disclosed
Capabilities Conversational AI, RAG, NL to SQL
Delivery Project-based delivery

Why couldn’t staff get a quick answer?

Staff at a large multinational molecular diagnostics enterprise couldn’t get quick answers, so the business wanted an enterprise AI assistant. Research, manufacturing, sales & compliance all run on shared information, & people wanted one place to ask, a single conversational AI interface.

That information sat in different systems, & most staff could reach only some of them. A question became a ticket, then the ticket became a wait.

  • A commercial lead wanted a warehouse metric, so an analyst wrote the query & replied the next morning.
  • A scientist needed one line from a protocol, so a colleague searched the document library by hand.
  • A market question meant browsing the web & pasting figures into a report, with no source recorded.
  • Every answer lived in a different tool, so people had to know where to look before they could start.

A language model can’t answer from a company’s own records by itself. Grounding it in the right documents keeps it accurate, as research on retrieval-augmented generation shows.[1]

Analyst at a desk writing a database query by hand, the slow manual work that routing questions to a data warehouse replaces
Before the assistant, a routine number meant an analyst writing a query & a wait for the reply.

What do teams usually try first?

Teams usually try one of four routes first, & each one is reasonable on its own.

Approach What it gets right Where it stops Who it still suits
Search each system by hand Nothing new to build Slow, only as good as the searcher Small teams, rare questions
A single chatbot on one dataset Fast answers in its lane Blind to every other source One knowledge base, one use
Hire more analysts Handles any question Cost grows with demand Deep, occasional analysis
A routing assistant, our route Reaches the right source Weeks of grounding & access work Many teams & sources, in volume

A model that picks the right tool & acts on it beats one that answers from memory alone.[2]

How we designed the assistant

Brainy Neurals designed one assistant that sits in front of every source & decides where each question belongs. A router reads the question, classifies it & hands it to the tool that can answer.

Structured questions go to the data warehouse as a generated query. Document questions run a search over the internal knowledge base, using retrieval augmented generation grounded in the client’s own files.

Live questions go to the web or a public data feed, & every answer comes back with its source attached.

Nothing reaches a source the user isn’t allowed to see, because access is checked on every request.

Diagram of the enterprise AI assistant: a question passes an access check, a router sends it to a structured, document or web tool, each tool reads its source, & an answer returns with its source SINGLE SIGN-ON CLASSIFY & ROUTE RETURN THE ENTERPRISE Question in Access check GROUP RULES Router Structured tool Data warehouse Document tool Knowledge base Web & live tool Live public data OUTSIDE THE ENTERPRISE Answer with source Diagram of the enterprise AI assistant: a question passes an access check, a router sends it to a structured, document or web tool, each tool reads its source, & an answer returns with its source Question in Access check GROUP RULES Router TOOL SOURCE THE ENTERPRISE Structured tool Data warehouse Document tool Knowledge base Web & live tool Live public data OUTSIDE THE ENTERPRISE Answer with source

Every question passes one router, & each source answers only what the user is cleared to see.

Routing over one index

The first decision was to route questions instead of building one giant index. A warehouse metric & a protocol paragraph need different proof, so the router picks the tool & that tool owns its retrieval.

Provenance is required

The second decision was to make provenance required. Every reply carries the document, the page or the query behind it, because an answer you can’t trace is an answer you can’t use.

Nothing reaches a source the user isn’t allowed to see, because access is checked on every request.

The technology stack we used

Each layer of the stack had to earn its place in a system that answers in seconds & never leaks a source. The document layer reuses the same intelligent document processing we build elsewhere.

Layer What we used Why What we ruled out
Language models A lighter & a stronger model Easy asks cheap, hard ones accurate One model for all
Question classifier Sorts simple from complex Speed on easy, depth on hard All traffic to one model
Router Picks the tool per question Reaches the right source One prompt for all
Document search Semantic search over the knowledge base Finds the passage, not the file Keyword search alone
Structured queries Plain language into warehouse queries Metrics without SQL Analysts writing each query
Ingestion Scheduled pulls from the documents Stays current Manual uploads
Access control Single sign-on with group checks Users see only what they can reach Trusting the interface
Delivery Answer plus source, table or chart Provenance on every reply Text with no basis

How does one question get answered?

One question passes through six steps, from the moment someone types it to the answer on screen.

  1. The assistant reads the question & classifies it, so it can pick a right-sized model.
  2. The router decides which source can answer: the warehouse, the documents, the web or a live feed.
  3. For a data question, the model turns the ask into a query, grounded on the schema & vetted examples.
  4. The chosen tool runs & returns raw results, either rows from the warehouse or passages from the knowledge base.
  5. The model writes a short answer over those results & attaches the source, page or query behind it.
  6. The exchange is saved to the user’s history, so the next question can build on it.
Six steps one question takes through the enterprise AI assistant: classify the question, route to a source, write the query, run the tool, answer with the source, save to history 01 Classify the question SIMPLE OR COMPLEX 02 Route to a source 03 Write the query SCHEMA & EXAMPLES 04 Run the tool ROWS OR PASSAGES 05 Answer with the source 06 Save to history HISTORY FOR THE NEXT QUESTION Six steps one question takes through the enterprise AI assistant: classify the question, route to a source, write the query, run the tool, answer with the source, save to history 01 Classify the question SIMPLE OR COMPLEX 02 Route to a source 03 Write the query SCHEMA & EXAMPLES 04 Run the tool ROWS OR PASSAGES 05 Answer with the source 06 Save to history HISTORY FOR THE NEXT QUESTION

One question travels from intent to a sourced answer without the user touching a system directly.

The user sees none of these steps, only the question they typed & a sourced answer.

The problems that nearly stopped us

Three problems nearly stopped the build, & they showed up in this order.

Queries that looked right

The model wrote queries that looked right & were wrong. It invented a column, joined the wrong table & returned a number the team couldn’t trust, because fluent output isn’t the same as correct output.[3]

One model, wrong source

One model tried to answer everything & picked the wrong source. Asked for a live figure, it answered from stale training memory, & asked for a document, it guessed instead of searching.

Slow & costly by default

Sending every question to the strongest model was slow & costly. A simple lookup waited behind the same heavy model as a hard analysis, & the bill climbed with every new user.

At this stage, clients often bring in specialist engineers instead of learning it the slow way.

How we fixed each one

We fixed each problem with a change that sounds simple, though each one took real testing to find.

We stopped the guessing

The model now sees the schema, sample rows & vetted example queries before it writes anything. A query that doesn’t run never reaches a user.

We split the job in two

A classifier sorts simple from complex, & a router decides which source to use. The model no longer answers from memory when the answer lives in a table.

We route by difficulty

Easy questions go to a lighter model & hard ones to a stronger model. The heavy model only runs when the question earns it.

None of this survives a slide deck. It survived an AI proof of concept against the client’s real data, where each failure showed up first.

Diagram of a grounded database query: the schema, sample rows & examples feed the model, the model writes one query for the database, & a query that does not run fails early Schema Sample rows Examples Model One query Database DOES NOT RUN, FAILS EARLY Diagram of a grounded database query: the schema, sample rows & examples feed the model, the model writes one query for the database, & a query that does not run fails early Schema Sample rows Examples Model One query DOES NOT RUN, FAILS EARLY Database

The model sees the schema & real examples before it writes a query, so a wrong guess fails early.

What changed after go-live?

After go-live, staff stopped queueing for answers & started asking one interface directly.

What Before After
Getting a metric An analyst wrote a query The user asks in plain language
Finding a document passage A manual search of the library A grounded search returns the passage
Where answers come from Scattered across separate tools One interface over every source
Trust in an answer No record of the source Every answer carries its source
Answering a routine question Hours or days, through a queue Seconds, self-served

We haven’t published a time-saved figure, though the client reports the drop is large. We won’t print a number we haven’t measured.

Day to day, a scientist or a sales lead asks a question & gets a sourced answer without opening a ticket. The people who used to answer those questions now handle the ones that are actually hard.

Does your team ask across many systems?

Tell us where your answers live today & what people keep asking. We’ll say whether a routing assistant fits, or you can check how ready your data is first.

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What is running today

The enterprise AI assistant runs in production for the client’s teams today. People across research, commercial & compliance ask it through one interface every day, & new sources arrive as new tools rather than rebuilds.

The client is a regulated business, so access checks & answer provenance were built in from day one, as AI in healthcare demands. A question never reaches a source the person asking isn’t cleared to see, & that rule hasn’t moved since handover.

Employee at a life sciences office desk asking a question through one conversational AI interface
Since handover, staff ask the assistant through one interface & get an answer with its source attached.

What we would do differently

We would carry four lessons from this build into the next one.

Ground the model first

We saw the confident wrong query early. The fix was never a better prompt, it was showing the model the schema & real examples first.

Split routing from answering

One model doing both jobs is a model that guesses. A classifier & a router are cheap, & they remove most wrong-source errors.

Make provenance mandatory

An answer without a source can’t be used in a regulated business, so we built provenance in from the start.

Test on the real data

Every failure that mattered showed up against the client’s own systems. None of them showed up on clean sample data.

An answer without a source can’t be used in a regulated business, so we built provenance in from the start.

Where else can an enterprise AI assistant fit?

The same routing pattern fits any team whose answers sit in more than one system, & it returns the origin of every answer.

Finance, insurance & legal teams face the same split, which is why the pattern suits AI in banking & finance just as well.

Diagram of the same routing pattern in finance: a question goes to the assistant, passes an access check, reads a core system, market data or filings, & returns an answer with its source Question Assistant Access check Core system Market data Filings Answer with source Diagram of the same routing pattern in finance: a question goes to the assistant, passes an access check, reads a core system, market data or filings, & returns an answer with its source Question Assistant Access check Core system Market data Filings Answer with source

The same routing pattern answers a finance question across separate systems & returns the source.

Industry The equivalent problem What changes in the build
Finance Answers split across systems & filings Tighter access, an audit trail per query
Insurance Underwriters hunt across policies & claims More document sources, a record of reads
Logistics Ops ask across a warehouse & the web Live feeds join the routing
Legal Teams search contracts & case files by hand Heavier retrieval, provenance per clause
Manufacturing Plant teams query production data & specs On-site sources, shop-floor wording

Porting it into AI in logistics or a new sector takes the client’s own sources & an access model. Each system needs a grounding pass before anyone trusts it.

Questions buyers usually ask

What is an enterprise AI assistant?

An enterprise AI assistant is one interface that answers questions from a company’s own systems & the web. It routes each question to the right source, & every answer comes back with a citation, so staff stop hunting across tools.

How is this different from a normal chatbot?

A normal chatbot answers from one dataset or a script & stops there. This assistant reads the question, picks among a warehouse, a document store, the web & live feeds, then grounds every answer in the source it used.

Can it query our database without writing SQL?

Yes, it turns a plain-language question into a database query, grounded on your schema & vetted examples. A business user gets a metric without writing SQL or waiting on an analyst.

How do you keep our data secure & answers auditable?

Access is checked on every request through single sign-on & group rules, so a user reaches only what they’re cleared to see. Every answer carries its source, which is what a regulated business needs to audit a reply.

How long does an AI assistant build take?

A working proof of concept on your own sources usually takes a few weeks. Grounding each source, wiring access & tuning the router each need a pass, & a production rollout follows once it holds up with real users.

How much does an enterprise AI assistant cost?

Cost depends on how many sources you connect, how clean they are & your access rules. Brainy Neurals scopes it from a short call, then quotes a fixed price. An AI readiness assessment tells you first whether your data is ready to answer.

What does your team keep asking about?

Describe the question your teams keep raising & where the answer lives today. One reply comes back from the person who would architect the build.







    Services behind this case study

    These Brainy Neurals services & industry teams built the assistant, from routing to document search.

    Conversational AI & AI agents

    The routing assistant pattern behind this build, from intent to the right tool.

    Generative AI applications

    Enterprise assistants & LLM apps built on your own data, not a public model’s memory.

    RAG development

    Document search grounded in your files, with the source returned on every answer.

    Document AI

    Ingestion & extraction that turn scattered documents into a searchable knowledge base.

    Hire AI developers

    Engineers who extend your team for the weeks when grounding & access fight back.

    AI in healthcare

    Assistants & document AI for regulated research, clinical & life sciences teams.

    An AI proof of concept is the fastest way to test this on your data, & AI consulting helps you choose what to connect first. An AI readiness assessment shows whether your sources can answer, & the industries we serve show where this already runs.

    Similar case studies

    These three Brainy Neurals builds share the same shape, & each one shipped into a real environment.

    AI Diet Assistant for Gastroenterology

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    Personalised AI Meal Planning for Chronic Care

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    Overhead Line Geometry Measurement

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    Cite this case study

    Shah, Rushabh. Enterprise AI Assistant for Sourced Answers in Life Sciences. Brainy Neurals, September 2026. https://brainyneurals.com/case-studies/enterprise-ai-assistant/

    Sources cited on this page

    1. Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, Kuttler H, Lewis M, Yih W, Rocktaschel T, Riedel S, Kiela D. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems. 2020, volume 33, pages 9459 to 9474. arXiv:2005.11401. DOI 10.48550/arXiv.2005.11401.
    2. Yao S, Zhao J, Yu D, Du N, Shafran I, Narasimhan K, Cao Y. ReAct: Synergizing Reasoning and Acting in Language Models. International Conference on Learning Representations (ICLR). 2023. arXiv:2210.03629. DOI 10.48550/arXiv.2210.03629.
    3. Ji Z, Lee N, Frieske R, Yu T, Su D, Xu Y, Ishii E, Bang YJ, Madotto A, Fung P. Survey of Hallucination in Natural Language Generation. ACM Computing Surveys. 2023, volume 55, issue 12, Article 248. DOI 10.1145/3571730.