edisyl — Your data isn't broken.

Where AI over data goes wrong

Valid query.
Wrong answer.

"Closed deal" means something different in sales than in finance. AI has no way to know. It queries correctly and returns an answer your CFO and your VP Sales would each dispute. They'd both be right.

What we built

A meaning layer that encodes how your organization defines its own metrics. The AI answers questions the way your business thinks, not the way a generic model guesses.

Technically correct.
Organizationally wrong.

The BI team spent a week on that report. The exec looks at it and says "that's not how we define retention." Everyone is right. The model just didn't know. Executives stop trusting the system. The data lake gets bigger. Confusion breeds disbelief.

What we built

We extract tribal knowledge: how your teams actually define your metrics. We bind it directly to the data. Agents stop guessing. They start answering.

The knowledge lives
in people's heads.

"Active user." "Healthy account." "Good quarter." The definitions exist, but they're in Slack threads, in the heads of the analyst who built the dashboard three years ago, in undocumented logic nobody wrote down. AI has no access to any of it.

What we built

We sit inside your existing stack and extract that tribal knowledge: structured, bound to the data, and growing smarter with every interaction.

What we do

We build the semantic layer your enterprise data is missing: the encoded understanding of what your data means, how your organization defines its metrics, where the knowledge lives that no schema captures. Once that layer exists, every AI tool you use answers questions the way your business actually thinks. The layer deepens over time. Every correction, every answered question enriches it. It becomes the most accurate representation of how your organization thinks about its data — and it belongs to you.

Intelligence

AI that answers like your organization thinks
When the semantic layer is in place, AI stops returning answers that are technically correct but organizationally wrong. The BI bottleneck breaks. Executives get answers they trust. The data team stops fielding the same questions every week.

A national arts organization · 807K contacts scored, 17K priority leads surfaced in 6 days
A creative-industry firm · relationship intelligence across contacts and deal flow

Infrastructure

Pipelines built on meaning, not just structure
The semantic layer informs every pipeline and transformation that runs beneath it. We connect disparate data sources and build the infrastructure on top of the same meaning layer: pipelines that know what the data means, not just what shape it's in.

A regulated financial institution · automated pipeline generation, 80%+ runnable output, 50%+ engineering time saved
Financial-infrastructure firms · multi-source data connectivity & agent fleet deployment · in discovery

How we work

We extract what lives
in people's heads.

The definitions that make AI trustworthy don't live in your schema. They live in the heads of your analysts, the person who built the dashboard three years ago and left, the undocumented logic nobody has time to write down. We extract that knowledge through a structured process and encode it into the semantic layer. What was implicit becomes explicit, machine-readable, and owned by your organization.

Meaning before agents.
Always.

Most AI deployments fail at the same place: they skip the semantic layer because it's the hard part, deploy tools on top of raw data, and get answers that are plausible but wrong. The model isn't broken. It just doesn't know what "churn" means at your company, in your data model, as defined by your VP of Product versus your CFO. The semantic layer solves this. Agents, pipelines, intelligence tools: all come after. This order is not optional.

The longer it runs,
the smarter it gets.

The semantic layer is not a one-time build. Every correction refines it. Every question answered deepens it. Stratum accumulates organizational intelligence continuously: new definitions, refined metrics, resolved ambiguities. After two years it is the most precise representation of how your organization understands its data that has ever existed. That asset belongs entirely to you.

We work inside your stack.

Nothing moves, nothing breaks.

The semantic layer lives on top of your existing stack. Your Snowflake stays. Your Salesforce stays. Your warehouse, your CRM, your pipelines: nothing moves, nothing gets replaced. We add the meaning layer your data was always missing. No migration. No rip-and-replace. No six-month implementation before the first answer. What changes is whether AI can understand it.

8
Years building data infrastructure at scale
7T+
Rows of data ingested and maintained
700M+
Entities scored and disambiguated
250+
Skills in the semantic layer

Chapter 02 · The Platform

The semantic layer. And everything it enables.

Stratum is the semantic layer at the center of everything edisyl builds: the encoded understanding of what your data means, how your organization defines its metrics, and where the knowledge lives that no schema captures. Forge builds agents equipped to act on that layer. Lattice orchestrates them as a fleet. The layer is the product. Everything else is what it enables.

"Without a semantic layer, every AI tool you deploy is guessing at what your data means. The right answer is knowable. It just has to be encoded first."

The architecture

Forge
Agent framework & specialized tools
Where agents are built and equipped. Each is purpose-trained with domain knowledge and specialized tools. Outputs are quasi-deterministic because agents use tools. They do not see raw data directly.

Lattice
Fleet orchestration & coordination
Coordinates agents as a fleet. Routes tasks, manages context handoffs, tracks the dependency tree. Agents run concurrently. Every action is SQL-traceable.

Stratum
Semantic intelligence & knowledge layer
Encodes how your organization understands its own data: what terms mean, how tables relate, where knowledge is complete and where it is not. The LLM touches interpretation in one controlled phase.

Stratum Layers of accumulated knowledge. The more the system runs, the deeper and more precise it becomes.

How a deployed fleet works · illustrative quantifiable outcomes

Scheduled or on-demand
Lattice orchestrated

Intelligence Manager

Owns coverage universe · routes tasks · compiles findings · triggers brief writer
Always on

Analyst 01

Data & scoring
Scores signals 1–5. Filters noise upstream.
Parallel

Analyst 02

Domain specialist
Trained on your context and decision logic.
Persistent memory

Analyst 03

Competitive tracker
Monitors comparable signals. Flags shifts.
Parallel

Analyst 04

Pipeline agent
Generates & validates pipelines. 80%+ runnable.
On trigger

Brief Writer

Formats for decision-makers · cites sources · delivers to inboxes, CRM, Slack
Weekly

Live deployments. Measurable outcomes.

The same three-layer system configured for what you actually need. Two orientations, both in production. Neither required starting from scratch.

"The architecture is consistent. The problem it solves is yours."

Growth Track · National Arts Organization

807,000 contacts scored.
17,000 priority leads surfaced.
The organization had over 1.2 million unstructured note fields in HubSpot that no tool had ever touched. We ingested all of it, built a lead scoring model trained on their specific signals, and delivered scored leads directly into HubSpot within six days. Daily write-back continues. Weekly hot-leads briefing posts automatically to Slack.

6 days
Time to first output
AUC 0.734
Model accuracy
807K
Contacts ingested & scored
Daily
Live score cadence

Efficiency Track · Regulated Financial Institution

DBT pipelines generated.
Engineering hours returned.
A regulated financial institution needed to automate the generation of DBT transformation pipelines from existing SQL logic — a task their engineering team handled manually at significant cost. Our pipeline-generation tooling automated the generation, validation, and iteration loop. 80%+ of output was production-runnable. Engineering time on this class of work dropped by more than half.

80%+
Runnable output rate
50%+
Engineering time saved

Chapter 05 · Foundation

Eight years building where it was hard.

Most AI companies were built in the last eighteen months. Edisyl was built over eight years on blockchain — the most unstructured, high-velocity, pseudonymous data environment that exists. 700 million entities. Seven trillion rows. Twenty chains. The tools and methodology we built there are what we deploy against your enterprise data today.

IP development by era

Patent
Patent US10706472B1 · Active · Filed 2019 · Granted 2020 · Expires 2039
Systems and Methods for Analysis of Digital Asset Development and Transaction Behaviors

Inventors: James F. Myers · David L. Balter · Eric C. Stone · Assignee: Flipside Crypto Inc (edisyl)

The entity-resolution and scoring techniques built for blockchain underpin the semantic intelligence layer we deploy across enterprise data environments today.

Status: Active
Expires: 2039

Proprietary tools, applicable anywhere

Pipeline Generation

SQL-to-pipeline compiler
Transforms query logic into production-grade transformation pipelines. Drives automated DBT generation: 80%+ runnable output, 50%+ engineering time reduction.

Query Translation

SQL-to-any-API translation
Agents query live data using familiar SQL syntax, no custom connectors required per source. The bridge between structured query logic and live production systems.

Scoring Methodology

Patent-backed entity scoring
700M+ entities scored and disambiguated across 20+ chains. The same methodology applies to any enterprise entity graph: customers, contacts, accounts.

edisyl · 2026

Talk is cheap.
Tell us what you have and what you're trying to accomplish. We'll take it from there.

Write to us.
A conversation. No agenda.