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Investors

Mechanistic AI.
Documented market need.

Diginetics builds Elijah Underwriting Intelligence: listing and deal intelligence for brokerages, CRE developers, lenders, acquirers, and insurers. Underwrite before sign. Guide pricing and DOM after go-live. Document parsing on MLS, county, and estate records. Regional brokerages live. CRE and lending pilots in motion.

Engine
5

Customer types: brokerages, CRE dev, lending, acquisitions, insurance.

Product
Live

Elijah Underwriting Intelligence with independent regional brokerages. Audit-ready cards and portal.

Round
Pre-seed

Patent filed. Selective partners. Deck and diligence on request.

Category
Specialist AI grounded in how systems behave.

Generalist models optimize for fluent language. Operators need answers they can open, challenge, and defend. We discover readable relationships in a client's data, validate them before anything ships, and deploy them through specialists that run as production infrastructure. Every anchor adds validated logic to a corpus that compounds inside the client's environment.

Market Need
The gap is documented. The category is open.

Operators already run SAP, Salesforce, SCADA, OSIsoft PI, and data platforms like Snowflake. They still cannot answer why output moved, which driver to act on, or what happens if they change a lever. Industry research and operator surveys describe the same missing layer we built.

Dashboards show movement, not mechanism

Industrial teams get alerts and KPI shifts daily. Root cause still requires engineers to manually trace signals across siloed systems. The pain is operational, not a lack of data volume.

Documented in McKinsey industrial analytics work and operator digital-maturity surveys: most value stalls between visibility and actionable cause.

AI pilots rarely reach accountable production

Predictive models flag anomalies but cannot defend a recommendation in a morning ops meeting. Teams revert to tribal knowledge when the model cannot show its logic.

McKinsey and BCG report a large share of industrial AI initiatives remain in pilot because trust, integration, and explainability lag behind model accuracy.

Black-box AI fails regulated accountability

High-stakes environments require auditable reasoning: energy production, hospital operations, insurance underwriting, public safety. Opaque scores do not survive governance review.

Gartner cites lack of trust and explainability as top barriers to AI adoption in operational settings. EU AI Act and sector guidance push toward traceable decision systems.

Forecasts without levers are not decisions

Teams need scenario planning: if uptime drops, if rates move, if supply tightens, what happens next. Point predictions do not answer which intervention changes the outcome.

Gartner's Decision Intelligence category exists because organizations need systems that connect data, logic, and action, not another chart layer.
What operators are asking for

A decision layer on top of systems they already run.

Not another dashboard. Not a chat wrapper on a public model. A layer that reads operational data, surfaces governing relationships, validates them before anything ships, and tells the team why, which lever to pull, and what happens if they move it. That is the documented need. Elijah is the product built to fill it.

TodayGapDiginetics
1SAP, Salesforce, SCADA, and Snowflake show that a metric movedElijah explains why it moved in readable logic
2Black-box ML flags risk without traceable causeValidated relationships your team can open and challenge
3Forecasts with no lever to pullScenario forecasts tied to operational drivers
Traction
Built and in motion.
Built
Discovery engine across thirteen industries
Same codebase. Provisional patent filed. Production AWS infrastructure with isolated client environments.
Live
Manifest with brokerages
Real estate specialist for pricing, targeting, and agent coaching. Production dashboards in operation.
Live
Enterprise anchor motion
Active pilots and deployments across energy, legal operations, cybersecurity, and commercial real estate. Status shared in diligence.
Moat
Why it compounds.

Cross-industry engine

Thirteen industries from one codebase. A competitor entering one vertical starts from zero on the rest.

Compounding libraries

Validated relationships persist in each client's environment. The library only grows. Retention is structural.

Patent filed

Self-driving discovery loop and cross-industry validation framework at the core of the platform.

Category timing

Decision intelligence and explainable operational AI are documented buyer priorities. The layer does not exist inside incumbents' dashboard stacks.

The Raise

Pre-seed open.

We are looking for partners who see mechanistic AI and the decision-layer category as durable, not a feature cycle. Request the deck for validation detail, anchor status, market evidence, and round terms.

michael.lazzarotti@diginetics.co

Questions, diligence, or a walkthrough of the market need and deck.