Artificial Intelligence Research
Advancing applied research in artificial intelligence for middle-market M&A, in particular proprietary market data, classification and buyer intelligence, to develop systems benefiting Navagant’s clients and deal teams.
Overview
Navagant maintains a dedicated artificial intelligence and machine learning function within the firm. Its work spans proprietary market and relationship data, classification and ranking models trained on the firm’s own transaction history, and applied systems that support live engagements.
The research function connects the firm’s transaction record, sector taxonomy, relationship graph and buyer intelligence into a common system. Companies, transactions, acquirers and decision makers can be classified against the same underlying structure, while new process activity adds to the record rather than remaining isolated within a single engagement. This creates a growing body of structured market knowledge that can be reused across research, valuation work, buyer identification and live transaction execution.
That approach is particularly relevant in middle-market M&A across education, training and workforce development, where target companies are often private, financial disclosure is limited and important information is dispersed across portfolios, relationships and prior processes rather than public filings. The systems are designed, trained and deployed internally on data the firm owns, with outputs tied back to their underlying evidence so deal teams can use the technology as part of the broader advisory process.
Research focus areas
Each area is in production and applied to live mandates. The work is built around the information problems that repeatedly arise in sector-focused middle-market transactions.
Transaction Intelligence
To maintain a comprehensive and automatically classified record of merger and acquisition activity across education, training and workforce markets, supporting valuation analysis, comparable transaction work and published research.
Sector Taxonomy and Classification
To define and maintain a proprietary sector framework, and to classify companies, transactions, acquirers and research against it consistently, so that every system in the firm reasons over the same structure.
Relationship Graph
To structure the firm’s accumulated record of firms, investment vehicles and decision makers, resolve entities that appear inconsistently across sources, and keep the graph current as roles and mandates change.
Acquirer Classification and Ranking
To identify sector exposure that firms do not state publicly, by reading portfolio holdings rather than stated thesis, and to rank acquirer fit on measurable weighted dimensions rather than assigned judgment.
Verification and Provenance
To trace every figure in a client document to its underlying source, flag anything unsupported before release, and record where each stored field came from and when.
How the work compounds
The focus areas are not independent. Each produces data the others train on, which is what allows the systems to improve with use rather than depreciate.
Research circulates
Sector studies are published from the transaction record, and engagement with them indicates where attention is concentrating.
A process runs
Every approach, response and outcome is recorded against the graph as it happens rather than reconstructed afterwards.
The record is maintained
Role changes, entity conflicts and stale entries are detected on a schedule and surfaced for correction.
The models recalibrate
Observed outcomes become the signal against which the next classification and ranking pass is calibrated.
Built to learn, explain and improve
The advantage comes from combining proprietary data, learning systems and transaction experience. Three principles guide how that capability compounds over time.
Outputs are traceable
Every ranking resolves to its component scores and every stored field to its source. A recommendation a client cannot interrogate is not a usable recommendation.
Models learn from outcomes
Completed processes, buyer responses and transaction outcomes feed back into classification and ranking systems, allowing the models to improve as Navagant’s proprietary record grows.
Judgment is amplified
AI surfaces patterns, relationships and evidence at a scale that would be impractical manually, giving deal teams a stronger information base for pricing, positioning, buyer selection and negotiation.
The team
The research function sits inside the bank and works directly with the deal teams whose engagements the systems support.
