AI CONVERSATIONAL ANALYTICS FOR BI WORKFLOWS

Turn complex data into clear, accurate answers, using natural language.
AI translates questions into insights, combining domain knowledge with structured analytics workflows.

Ask questions, get answers

A natural language interface that lets experts query complex datasets without writing code, SQL, or navigating multiple dashboards.

Domain-aware intelligence

The system adapts to domain-specific concepts, enabling accurate analysis across use cases such as healthcare, pharma, and beyond.

Built-in analytical guardrails

Domain-specific rules prevent common statistical mistakes, ensuring answers are trustworthy out of the box.
‍

THE PROBLEM

Organizations across regulated industries work with complex datasets, but extracting insights remains slow, technical, and error-prone.

Analytics requires specialists

Business users rely on data analysts or BI (Business Intelligence) teams to answer even simple questions, creating bottlenecks and delays.

Critical domain knowledge is not always documented

Correct analysis depends on understanding specific rules, definitions, and methodologies, which can vary across teams and industries.

Pre-built dashboards only answer predefined questions

When new questions arise, custom queries are required, which take time to create and validate.

Errors are silent and costly

Incorrect methodologies or missing steps can produce plausible-looking results that lead to incorrect decisions.

Applicable across domains

Used in domains where complex data needs to be queried, interpreted, and validated before use: pharmaceutical companies (prescription analytics, market access, medical affairs), health insurers (claims analysis, cost modeling), hospital networks (treatment pattern analysis, resource planning), contract research organizations (real-world evidence, patient flow studies), and health authorities (epidemiological monitoring, drug utilization reviews).

THE SOLUTION

An AI-powered conversational analytics system that translates natural language questions into accurate, domain-aware answers, drawing from both pre-built analytics APIs and raw datasets.

Core capabilities

STEP

Natural language querying

Users ask questions in plain language and receive answers with supporting data, charts, and explanations. No SQL, no API knowledge, no dashboard navigation required.

STEP

Semantic data discovery

The system automatically identifies which data sources, APIs, and metrics are relevant to a question using vector-based semantic search, not rigid keyword matching.

STEP

Domain-specific skill system

Modular analytical skills are applied based on the question, adapting to the required methodology depending on the domain and use case.

STEP

Ad-hoc data querying

For questions beyond pre-built metrics, the system generates and executes queries directly on underlying datasets, enabling deeper analysis when needed.

STEP

Visualization and interpretation

Results are presented with charts, graphs, and explanations when appropriate, helping users understand data without additional tools.

STEP

Market extrapolation

Correctly scales sample domain-specific data to
full-market estimates using statistical extrapolation weights, applying the right methodology automatically.

What sets it apart

Analytical guardrails by design

The system enforces domain-specific rules that prevent common analytical errors, ensuring results are accurate and consistent.
‍

Skills-based analytical architecture

Analytical knowledge is modular and applied dynamically, allowing the system to adapt to different question types and domains.
‍

Dual-mode flexibility

The same interface handles both quick dashboard-style queries (via pre-built APIs) and deep research queries (via direct SQL), automatically routing to the right approach based on the question.

Provider-agnostic AI layer

The underlying language models can be swapped or combined (the system currently uses both Google Gemini and Anthropic Claude) without changing the analytics logic or user experience.

See how this would integrate into your current architecture

How quality is measured

Evaluation

The system uses a multi-layered evaluation strategy that tests both the AI's ability to select the right data sources and its ability to produce correct analytical results.

Dataset approach

Curated question-answer pairs organized by analytical capability (metric selection, filter extraction, API routing)
Each test case includes the user question, the expected data source or API, the expected filters, and the expected result characteristics
Datasets are versioned and grow as new edge cases, question types, and analytical patterns are encountered

Online validation

User feedback (thumbs up/down with optional comments) is captured on every response and linked to the AI's processing trace. Feedback scores are tracked over time to detect quality regressions, with automatic alerting when accuracy drops below thresholds

Key metrics

Data source accuracy measures how correctly the system selects relevant data inputs
Filter correctness evaluates whether queries apply the correct constraints
Analytical correctness (LLM judge) evaluates whether the retrieved data actually answers the user's question, catching cases where the right data source is selected but the wrong slice is returned
User satisfaction reflects real-world performance across different use cases

Why this approach

This multi-stage evaluation catches errors at different levels (routing, filtering, interpretation), not just end-to-end
LLM-as-judge evaluation handles the subjective cases where the same question can be answered correctly in multiple ways
Trace-level observability (via Langfuse) enables root-cause analysis when quality issues are detected

Architecture

Core System Integrations

Healthcare Analytics Platform

Two-way data flow: pulls dashboard metrics, widget configurations, and filter options; pushes AI-generated insights back to the user interface.

AI Models (LLM)

Multi-provider architecture using large language models for question understanding, data retrieval orchestration, SQL generation, and answer synthesis.

Data Warehouse

Direct SQL access to raw datasets for
ad-hoc research queries, with
session-scoped materialized tables for performance.

Vector Store

Semantic search index over available APIs and metrics, enabling the system to find the right data source by meaning rather than exact keyword match.

Observability & Tracing

Full processing traces for every conversation turn, including prompt versions, model responses, tool calls, and quality scores.
‍

Relational Database

Stores conversation history, user feedback, agent checkpoints, and session state for continuity across interactions.
‍

IN PRODUCTION

It's already running in a pharmaceutical company

Confidential· Germany · Pharma

We turned complex datasets into structured, validated insights through natural language interaction.

The system combines semantic data discovery, domain-aware analytical skills, and AI-powered data analysis workflows to answer both simple and complex questions.

Each step is coordinated within a structured system, ensuring results are accurate, traceable, and aligned with domain requirements.

The result is faster access to insights, fewer analytical errors, and a system that applies domain knowledge consistently across use cases.

Technical case-study coming soon

Clear answers FOR

Common Client Concerns

”How do we know the AI's numbers are correct? We can't afford wrong data in our reports.”

CORE FEAR

AI-generated analytics silently producing incorrect numbers that get used in business decisions, regulatory filings, or market assessments.

How it’s addressed:

Analytical guardrails are enforced at the system level, and the AI cannot bypass rules such as "always use extrapolation weights for patient counts" or "never use COUNT(*) as a patient volume metric." These are not suggestions to the model. they are structural constraints.
Every query includes the correct statistical methodology (extrapolation, insurance splits, deduplication across time periods) as non-optional steps, eliminating the most common classes of analytical errors.
Results are validated against pre-built dashboard metrics where overlap exists, providing an automatic cross-check.
Full traceability: every answer links back to the exact query, data source, and methodology used, making verification straightforward.

”Our analysts have deep domain expertise. Will the AI oversimplify their work?”

CORE FEAR

The system is too basic for power users and becomes another tool they have to work around rather than with.

How it’s addressed:

The dual-mode design explicitly supports both quick lookups (dashboard mode) and deep research (SQL mode). Power users can ask complex, multi-step analytical questions and get raw SQL-level results.
Conversation continuity allows iterative refinement: analysts can start broad, drill down, adjust parameters, and explore alternative cuts of the data in a single session.
The AI shows its work: SQL queries, methodology choices, and assumptions are transparent, so analysts can verify and learn rather than blindly trust.

“We need this to work with our specific market definitions and data structure"

CORE FEAR

The system is a generic tool that doesn't understand our particular therapeutic areas, market segments, or data conventions.

How it’s addressed:

Market definitions are configured per project, not hardcoded. The system adapts to whatever market structure the client defines.
The knowledge system (schema, glossary, skills) is modular and can be extended with client-specific terminology, business rules, and analytical patterns.
Session-scoped data materialization means each user works with their specific market view, not a generic dataset.
Prompts and analytical rules are managed externally and can be updated without code changes, enabling rapid adaptation to new therapeutic areas or regulatory requirements.

Ready to move from AI experimentation to secure production deployment?

Let’s build agentic systems that are reliable, compliant, and built to scale.

Get in touch