What this is
edisyl makes unstructured and complex data usable by AI agents. We've built a three-layer system — ingestion, semantic understanding, and coordinated agent deployment — that enables the data enterprises already have to be performant.
"Every enterprise has data. Almost none of it is AI-ready. And the gap between having data and acting on it is where most AI investments die."
Problem
Architecture
Now
Time to stop wondering if your numbers are right or why your AI is 'hallucinating', so you can focus on results.
Data exists.
AI can't use it.
The bottleneck in enterprise AI is not the model. It is everything underneath the model. Enterprises have years of accumulated data — in CRMs, email archives, note fields, document repositories, warehouses — and almost none of it was collected with AI in mind. Unstructured, disconnected, inconsistent. Agents guess at it. They break against it. And AI initiatives stall here before anything meaningful happens.
"Better models do not fix unstructured data. Faster infrastructure does not fix it. A different architectural approach does."
- The data problem is not a storage problem and it is not a model problem. It is a preparation problem. Most enterprise data was collected across many systems, over many years, without any consideration for how an AI agent would need to access or interpret it.
- Unstructured data is the harder version of this problem — and the less solved one. The majority of an organization's most valuable information lives in notes, emails, documents, and conversations that have no schema, no labeling, and no consistent format.
- The organizations that solve this first will have a structural advantage. The ones that don't will keep launching AI pilots that stall before they deliver anything measurable.
Three layers.
One system.
Each layer depends on the one before it. All three together is what makes agents reliable at enterprise scale. Most organizations trying to deploy AI are starting at layer three — the agents — without having built layers one and two. This is why they fail.
"The architecture is the argument. Agents without a semantic layer are guessing. Agents without clean data underneath are guessing at noise."
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01Data Ingestion
Extract and standardize data from wherever it lives — structured databases, CRM systems, cloud warehouses, document repositories, email archives, and unstructured note fields. The result is a unified, agent-accessible data layer. This is the step most organizations skip. It is the reason most AI deployments fail.
Structured Unstructured CRM Documents Warehouses -
02Semantic Layer
Encodes how an organization understands its own data — what terms mean, how categories relate, what a high-value signal looks like in context. Agents stop guessing and start interpreting the way a domain expert would. This is what separates an agent that produces outputs from one that produces the right outputs.
Org Logic Definitions Context Scoring -
03Lattice — Agent Fleet
Coordinated agents with persistent memory. Unlike off-the-shelf agents that lose context as tasks grow, Lattice saves every completed step. Any agent picks up exactly where the last one left off. This makes multi-day, multi-step enterprise workflows reliable — something that is simply not possible with standard agentic tooling today.
Persistent Memory Multi-Agent Long-Running
Two patterns.
Both in production.
We focus on two validated application lanes. Both draw on the same underlying architecture. Both are in active deployment with enterprise clients today. Not prototypes. Not pilots awaiting sign-off. Working deployments producing measurable outcomes.
"The proof is not that the architecture is clever. The proof is that it works — on real data, for real organizations, in days rather than months."
Years of contact history. Unscored. Unranked. Invisible to any agent without the right preparation underneath it. An edisyl agent fleet ingested 807K records, learned how the institution defined donor value, applied that semantic layer to the entire contact base, and surfaced 17,000 high-priority leads — written back into HubSpot, ready to act on, in six days.
Enterprise data engineering teams spend the majority of their time on work that is complex but not creative: building transformation pipelines, writing boilerplate code, validating outputs at scale. edisyl agents take this on entirely. Given the data environment and a specification, the fleet generates production-ready DBT pipeline code and iterates on quality with a consistency human teams cannot match at volume.
Eight Years.
AI Finally Caught Up.
For eight years, Flipside built and operated data infrastructure at a scale most enterprise teams never encounter. Working with blockchains (the most complex data set imaginable) we maintained 7 trillion rows of data, and resolved over 700 million data entities across more than 20 networks. We learned how to make messy, high-volume, partially structured data usable at speed. The enterprise AI market has spent two years discovering that model capability is not the constraint. We have been solving the actual constraint since 2017.
"The companies winning in 2026 are not the ones who deployed the most AI tools. They are the ones who did the hard infrastructure work first."
- The enterprise AI market is at an inflection point where the bottleneck has shifted from model capability to data infrastructure and agent orchestration. This is not a theoretical argument — it is what [Major Consulting Firm], [Major Financial Institution], and others are discovering in their client deployments right now.
- Flipside's eight years working at scale with complex, high-volume data across more than 20 networks was direct preparation for this moment. The instincts, the tooling, and the team are not borrowed from adjacent fields. They were built specifically for this class of problem.
- The window for establishing a differentiated position in enterprise AI infrastructure is open. It will not stay open indefinitely. The organizations that move now will define the standard. The ones that wait will find the standard already set.
Practitioners.
Not theorists.
These are people who have shipped things at scale, worked with major enterprises, and are building this from conviction and evidence — not trend-following. The claims in this document are not hypothetical because of the people behind them.
"The best argument for the product is the team that built it. Their track record precedes what they're building now."
The team is built around two deep capability clusters. Combined, they represent the full stack required to deploy AI agents on complex enterprise data — from raw infrastructure to applied intelligence to client outcomes.
- CTO & Co-FounderPluralsight · Smarater
- COOdunnhumby (Global Head Product Eng)
- Director, EngineeringUKG · Brightcove · Hasbro
- Principal EngineerPromobase
- Principal Software EngineerDraftKings · Mil Crypto Lab
- Senior Software EngineerDraftKings · Rainier
- Software EngineerDataAssembly · AECOM
- Senior DevOps EngineerFinancial Recovery Tools (Fin)
- VP, Data Science (PhD)Good Judgment Inc · TD Bank
- Senior Data ScientistFlipside (Data Lead)
- Chief Data Scientist & Co-FounderPluralsight · USAA Statistics
- VP, Data AnalyticsPluralsight · MBA · Smarater · BotAgent
- Manager, Data AnalyticsMcKinsey · Framebridge · Catalant
- Manager, Analytics EngineeringSSB (BI Manager) · DIRECTV
- Manager, Analytics EngineeringRazor · Syndicate · Chainlink
- Senior Analytics EngineerNF Think · Barokaidi · Couaang
- Analytics EngineerMino Games
- Junior Analytics EngineerPurdue (Blockchain Instructor) · Xanga
- Senior Data ScientistKeybank (Quantitative Risk)
- Principal, GTM EngineeringCyCognito · Kinsa · Puppet
The right conversation
is not a demo.
We forward deploy AI data specialists to understand and build POCs for enterprises grappling with large complex data sets, where unstructured information creates opportunities for solutions.
the real problem.
If you are a technology or strategy leader who has hit the data wall on an AI initiative, or an insurmountable data challenge, we'll bring expertise to your doorstep, and solutions that can be customized to your needs.
[email protected]- A first conversation focused on understanding your context — not presenting ours. We want to understand what you're trying to accomplish before we say anything about what we've built.
- If there's a fit, we'll show you the architecture in depth and walk through the proof points in detail. We can move fast once we understand the problem we're solving together.
- We are selective about who we spend time with. If this document resonated, that is already a signal worth following up on.