Who we are

Engineers, data scientists, and builders working on one of enterprise AI's hardest problems.

edisyl came out of eight years spent inside the messiest, fastest-moving data environment we know of. The tools we built, the methods we figured out, and the patent that came with them are what we now bring to enterprise data, exactly as it lives in the wild.

We agree on what success looks like before any work starts. Scored leads written to CRM. Pipelines running in production. Briefings landing in the inbox of the person who has to make the call. No pilots designed to go nowhere. No reports that sit in a folder.

Key Metrics

• 8 Years building data infrastructure at scale
• 7T+ Rows of data ingested and maintained
• 700M+ Entities scored and disambiguated
• 250+ Agent skills actively orchestrating

How we work

Outcomes, not reports.

We work with data as it actually is.

Messy, incomplete, unstructured. We don't ask clients to clean it up, change how they work, or move anything. We build on top of what's already there.

We obsess over output quality.

Most AI deployments fail quietly. We've spent years figuring out where they break and building the harness that prevents it: meaning layers, validated tools, and a dependency graph that keeps every agent grounded in something real.

We deliver outcomes, not reports.

Every engagement is shaped around a concrete output: scored leads in CRM, pipelines in production, automated briefings going to the people who make decisions. If we can't measure it, we don't ship it.

We go to where the data lives.

CRM, warehouse, email, documents, APIs. Most organizations have data spread across systems that were never built to talk to each other. We build the connective tissue that lets them.

Open roles

Join us.

If you've watched AI fail on data and have a strong opinion about why, we'd like to hear from you.

Role 01

VP / Director of Finance

You own the day-to-day finance operations and partner with the CFO on the numbers that decide where the business goes. Controller brain and FP&A brain, one person.

Details

• Type: Full-time
• Work style: Remote-first
• Reports to: CFO

What this actually is

• You own day-to-day financial operations end-to-end: month-end through year-end close, management reporting, internal controls, payroll, and treasury.
• You're a core strategic partner to the CFO on budgeting, forecasting, long-range planning, and the unit economics that tell us whether we scale.
• You own SaaS metrics from scratch (ARR, NRR, churn, CAC, LTV, payback) plus AI-credit and consumption-based revenue accounting and ASC 606.
• You manage a small team (one internal accounting FTE plus an outsourced bookkeeping firm) and own the finance tech stack as it gets built.

What we're looking for

• 10+ years in finance. Spanning controller and/or FP&A work. You've done both, not just one.
• Startup experience. You've built finance processes from scratch in a fast-moving company and know what that actually requires.
• SaaS metrics command. You can build and explain ARR, NRR, CAC, and LTV from scratch, not just pull them from a dashboard someone else built.
• Hands-on operator. You do the work yourself, not just direct others. That's a requirement, not a preference.
• Financial modeling fluency. Budgeting, forecasting, variance analysis, scenario planning. QuickBooks, NetSuite, or similar, plus Excel and Sheets at a high level.

Role 02

Enterprise Solutions Strategist

You build relationships and close enterprise deals within our partner network's account base, alongside the CEO. Founding GTM partner, not a quota-carrying AE.

Details

• Type: Full-time
• Work style: Remote-friendly
• Role: Founding GTM

What this actually is

• You work directly with the CEO to build relationships and close enterprise deals within our partner network's existing account base. Not cold outreach: navigating a complex partner organization to earn the right to be in the room.
• You build and maintain relationships with internal account partners, learn their priorities, and find creative entry points when the obvious path is blocked.
• You run discovery with client stakeholders and translate it into a commercial narrative, then own the deal mechanics: proposals, SOWs, procurement sequencing, contract navigation.
• You document what works and enable partners to carry smaller opportunities themselves, so the motion becomes a repeatable system, not a series of one-offs.

What we're looking for

• Channel sales experience. You've sold through partners protective of their own accounts, and found side doors when the front door was closed.
• Technical fluency, not technical depth. You can hold your own in a conversation about data pipelines and AI systems without needing to build them yourself.
• High EQ. You read rooms, tell a champion from a blocker, and adjust your approach without losing your point of view.
• Deal instinct. You think in economics: services vs. software mix, margin, expansion triggers, long-term account value.
• Written clarity. Proposals, SOWs, and deal memos that people actually forward to their colleagues.

Role 03

Forward-Deployed AI Data Engineer

You go in. You figure out what the data actually looks like. You make the agents work against it.

Details

• Type: Full-time
• Work style: Remote-first
• Role: Forward-deployed

What this actually is

• You embed inside client environments (CRMs, warehouses, email archives, document repositories) and make AI agents work against data that was never prepared for them.
• You're not building generic tooling. You're solving a specific problem for a specific organization, with whatever data they actually have.
• You work directly with our agent framework, orchestration layer, and semantic intelligence system.
• Every engagement ends with something measurable: leads written to CRM, pipelines running in production, briefings delivered to decision-makers.

What we're looking for

• Unstructured data instincts. You've worked with data that had no schema, no labeling, no consistent format, and you didn't flinch.
• Agent-building experience. You know where LLMs break against data problems. You've hit the accuracy cliff and built around it.
• SQL fluency. You think in queries. You use DuckDB, dbt, or similar without needing to look anything up.
• Client-facing comfort. You can sit in a room with a CTO and explain why their data isn't AI-ready without making them feel bad about it.
• Bias toward output. You care more about whether the agent's results were right than whether the code was elegant.

Role 04

Enterprise Data Strategist

You understand the client side. You've sat inside the organizations that have the problem. Now you help solve it.

Details

• Type: Full-time
• Work style: Remote-first
• Role: Client-facing

What this actually is

• You're the first person a new client talks to. You understand their data environment, their team structure, their actual friction, not their stated problem.
• You translate between what they think they need and what will actually deliver a measurable outcome.
• You scope engagements around the "named person with the named pain." No generic AI evaluations. No pilot programs designed to go nowhere.
• You stay involved through delivery, ensuring what gets built matches what was promised.

What we're looking for

• Enterprise data fluency. You know what a CRM actually looks like inside a $2B company. You understand data warehouses, pipelines, and why they were built the way they were.
• Consulting or internal strategy background. You've scoped and delivered complex data projects. You know the gap between a proposal and reality.
• Sharp diagnostic instincts. You can walk into a new environment and identify within two conversations where the real problem is.
• Comfort with ambiguity. Enterprise data environments are not clean. Neither are the conversations around them.
• Outcome orientation. You measure success by whether something changed in the client's business, not whether the project was delivered on time.

Role 05

AI Agent Architect

You design the systems that make agents reliable at scale. Not just functional. Trustworthy, auditable, and repeatable.

Details

• Type: Full-time
• Work style: Remote-first
• Role: Systems-focused

What this actually is

• You design the architecture that makes agent fleets reliable: the harness, the tooling, the orchestration patterns, the meaning layers that keep outputs grounded in organizational context.
• You work on our agent framework, fleet orchestration, and semantic intelligence layer, building and extending the systems that production deployments run on.
• You care obsessively about output quality. Not because someone told you to, but because you've seen what happens when agents drift.
• You solve for quasi-determinism: agents that use validated tools instead of guessing at raw data, producing consistent and auditable results at scale.

What we're looking for

• Agent systems depth. You've built multi-agent systems that run in production. You understand context windows, memory management, dependency graphs, and where things break.
• LLM failure mode literacy. You know the accuracy cliff. You know why prompting cannot fix semantic problems. You build systems that don't rely on the LLM getting it right every time.
• Data engineering background. SQL, DuckDB, dbt, APIs. You think in pipelines and can instrument everything.
• Strong opinions on agent design. Specifically, you have a view on why most agent architectures fail at enterprise scale, and you've built something that doesn't.
• Production instincts. You don't consider something done until it's been wrong three times and you've fixed it twice.

Benefits

What you get.

Remote-first. A senior team. Real ownership of the work you ship.

1. Health
   • Medical, dental & vision
2. Time off
   • Unlimited vacation + 10 holidays
3. Retirement
   • 401(k) retirement savings
4. Stipends
   • Home office, training, wellness & wifi
5. In person
   • Company offsites & team meetups
6. Workstyle
   • Remote-first, senior team