Advisory and engineering to put AI to work inside the systems you already run on — grounded in your data, governed by your rules.
The ambition is real and the budgets are growing. What is missing is rarely the model — it is the readiness underneath it. Four patterns show up again and again.
A demo impresses, then dies in a sandbox. No path to production, ownership, or budget.
Models are only as good as what feeds them. Messy or unpermissioned data quietly caps every use case.
AI bought as a side app, disconnected from the systems where finance and operations actually run.
No defined metric for success, so nobody can say whether it worked — and the next round of funding stalls.
We don’t sell AI for its own sake.
We embed it into the systems where you run operations and finance — on real data, real workflows, with oversight built in. That grounding is what separates durable results from demos.
Every initiative ties to a measurable result — cycle time, cost, accuracy, or revenue.
AI is most valuable on clean, governed data in your core systems — the layer we already own.
We design for oversight wherever money, compliance, or customers are involved.
Security and permissions are designed in from the start, never bolted on.
Four stages, in order. Most engagements start at Assess and stop wherever the value stops being real — we would rather end at stage two with an honest answer than build something you do not need.
Where AI creates measurable value in your operation, and where it is the wrong tool. Readiness scoring, opportunity discovery, ROI modeling.
You get a scored readiness report and a prioritized use-case shortlist.
The architecture before the project. Reference design, data and integration patterns, model selection, security, permissions, and governance.
You get a reference architecture, a governance policy, and a phased roadmap.
Agents that take action, automation across finance and operations, assistants on your governed data, and forecasting from your own systems.
You get working systems inside the platform you already run.
Training, monitoring, drift detection, and measurement against the baseline we set at Assess. A working model nobody uses is not a win.
You get adoption, and a measured result you can put in front of a board.
A structured, low-risk first step. We score where you stand across five dimensions, then turn the result into a plan — before anyone writes code. Fifteen questions, a four-point scale, an overall score plus a per-section read.
AI strategy, leadership commitment, defined success metrics.
Data quality, system integration, security governance.
Change adaptability, collaboration, tolerance for experimentation.
Technical capability, learning culture, identified change champions.
Process documentation, automation opportunities, pilot readiness.
Overall and per-dimension scores, with a clear read on strengths and gaps.
High-value, low-risk opportunities ranked by impact and feasibility.
A sequenced plan — what to pilot, what to scale, and what to govern.
An agent takes action toward a goal — reading data, calling tools, updating systems — not just answering questions. Delivering that safely in production takes a disciplined lifecycle.
Bounded, high-value, measurable use cases, with clear limits on what the agent can and cannot do.
Permissioned tools and connectors, encoded policy, and human approval on anything sensitive.
Test real scenarios and edge cases before anything touches production.
Roll out narrow, watch for drift, and improve continuously.
The most immediate value comes from AI built into the systems you already run, not a separate app to maintain.
Data entry, reconciliation, reporting, and document handling — the repetitive work that scales badly with headcount.
Action-taking agents that update your core platform under guardrails, not a bolt-on chatbot.
Connected through role-based permissions and OAuth, so your own access controls govern what the AI can see and do.
Extract, classify, and route invoices, contracts, and forms straight into your systems.
Forward-looking signals from your business data, for finance and operations leaders.
Generic AI shops start from scratch. We start inside your systems of record, where the governed data, the workflows, and the access controls already exist.
AI runs on the clean, permissioned data already inside your core systems.
Access is governed by your systems’ role-based permissions and OAuth.
For business use we recommend AI platforms on plans that do not train on your data.
The engineers who run your NetSuite, Oracle, Salesforce, and Rootstock builds are the ones who build the agents.
From foundation models, to the tools that build with them, to the platforms you run on — we choose what fits the problem.
A few weeks gets you a scored view of where you stand and a prioritized roadmap for where AI actually creates value in your business.
Book a readiness assessment