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team@kovazu.comEnterprise AI Knowledge & Research Platform
Global Management Consulting Firm
Key Outcome
60% reduction in internal research time — onboarding from 3 weeks to 4 days
The Challenge
The firm had accumulated 8 years of high-value proprietary research, frameworks, and engagement documentation — over 40,000 documents — that was effectively inaccessible. New consultants spent their first three weeks being manually briefed. Senior consultants were re-doing research that had been done before, unknowingly. The firm was paying for external research databases when the answers were already in their own archive.
Our Approach
We designed the platform around three core use cases validated in discovery: search ("find everything we know about X"), synthesis ("summarise our position on Y"), and generation ("draft a section of this report using our methodology"). Access controls mirror the firm's existing permission structure, so sensitive engagement data is only surfaced to consultants with appropriate clearance. The platform was built to be operated and extended by the firm's internal IT team post-handoff.
Tech Stack
System Architecture
How the system flows
Build Pipeline
How We Built It
Document Corpus Audit & Ingestion
Audited 40,000+ documents across 7 internal systems (SharePoint, Google Drive, Confluence, email archive). Built a classification layer to identify document type, sensitivity level, and practice area. Documents are ingested incrementally — new documents added to connected systems are automatically indexed within 15 minutes via webhook-triggered pipelines.
Hierarchical Chunking Strategy
Research documents required a different chunking strategy than standard text: executive summaries, methodology sections, and data tables needed to be treated as distinct semantic units. We implemented hierarchical chunking that preserves document structure, with parent-child chunk relationships maintained in Weaviate's reference architecture for context-aware retrieval.
Access-Controlled Vector Index
Each document chunk is stored with access metadata (team, engagement code, sensitivity classification). Queries are filtered at the vector database level before any content is retrieved — ensuring that a consultant searching for "pharmaceutical client strategy" only surfaces documents their permissions allow. No post-retrieval filtering that could leak metadata.
Multi-Mode Query Interface
The search interface supports three query modes: keyword (BM25), semantic (vector), and hybrid (weighted combination). Query expansion using GPT-5.5 reformulates ambiguous queries before retrieval. Results are grouped by document type and ranked by semantic relevance, recency, and internal citation frequency.
AI Synthesis & Report Generation
A synthesis mode takes a user-defined research question, retrieves the top-20 most relevant chunks, and generates a structured analysis with in-line citations to source documents. The generation prompt is tuned to the firm's house style and explicitly instructs the model to distinguish between firm-established positions and its own inferences.
Onboarding Knowledge Delivery
A dedicated onboarding module delivers structured learning pathways for new consultants: day-by-day content sequences built from the firm's own methodology documentation, with an AI tutor layer that answers questions using only firm-approved content. Progress tracking and comprehension checks are built in.
Results
What We Delivered
60%
Research Time Reduction
Measured across a 90-day cohort of 40 consultants — average time spent on internal knowledge retrieval dropped from 5.1 hours per week to 2.1 hours.
4 days
New Consultant Onboarding
Onboarding time for new hires dropped from 3 weeks to 4 days, with measured knowledge assessment scores 22% higher at the end of onboarding.
40K+
Documents Indexed
The full 8-year document corpus is now live, searchable, and actively used — with 340+ active users in the first quarter post-launch.
LET'S WORK
TOGETHER
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