Put AI where yourbusiness gets stuck.
We build AI into the jobs that slow your team down and cost you money, then show you what it's worth in hours and dollars.We map the workflow, connect it to your systems (API where there is one, browser automation where there isn't), run the repeatable steps as governed agents, and log every run, model and dollar to an ROI dashboard your finance team can pull themselves.
The money leaks out while work waits.
It leaks into the days work sits in someone's inbox, the answers only one person has, and the decisions nobody checked. Pick one below and we'll show you what it's costing you right now.
None of that is a model problem. It is a work-clarity problem.
Once the work is clear, connected to the right information, and governed by the right approvals, AI can carry the repeatable parts.We don't count agents deployed. We count which of these six numbers moved.
And we agree on how to measure them before we start. Every XSparks workflow has to improve at least one. Finance agrees on the definition before anything ships.
- 01↓
Cost down
Less time and money spent on repetitive operating work.
- 02↑
Revenue up
Fewer missed follow-ups, faster quotes, and better renewals.
- 03↑
Hours back
Routine work comes off your team's desk, permanently.
- 04↑
Capacity up
More output without automatically adding headcount.
- 05↑
Quality up
Fewer errors, less rework, fewer escalations landing on someone's desk.
- 06↓
Risk down
Every step gated, logged, and traceable so you're always in control.
Four things, and none of them are complicated.
There's real engineering underneath. Here's what it means for your business day to day. Keep scrolling, or jump to whichever step you're curious about.The same four things, described the way your systems person will ask about them. Keep scrolling, or click a step.
- 01 · Understand
We give it the context to understand your business specifically.
Not how businesses generally work. Your customers, your products, your people, and how they all connect. Every document, process and rule it has been taught, observed and budgeted in one place.
A governed context layer built from your documents, systems and people.
Product catalogues, pricing rules, SOPs and CRM/ERP records are processed into a versioned context store with per-team access. It sits in your own cloud account, in the region you choose, on your bill. Whole documents never reach a model: what leaves is extracted context, with PII redaction available on the way out. We do not train or fine-tune on your data. Roll a context back a version and every workflow reading it stops and replays.
Live XSparks platform · sample workspace data
- 02 · Think
It works out what to do.
The right AI picked for each job. You keep your own accounts and pay your own bill at cost. We never mark it up.
Model routing per task, on your own accounts.
One workflow runs many tasks of different complexity, so the model is chosen per task, not per workflow, from a task table we set with you during discovery. Pin your own model if you'd rather. You hold the API keys, so your provider bills you directly and we never touch the token spend. Keys you ask us to hold sit in an encrypted, pen-tested vault. Swap providers without rebuilding the workflow.
Live XSparks platform · sample workspace data
- 03 · Execute
It does the work.
Real jobs running across the systems you already rely on, even the fifteen-year-old one without a clean API. What's running, what's paused, what's waiting on a person.
Agents that act through APIs, and through the browser where there is no API.
Connectors for common ERP, CRM and email. A browser worker for legacy and green-screen systems. Every run is traced end to end, and runs, queues and failures are visible per team. One kill switch stops any workflow or agent mid-run, across the parent company and every child company under it.
Live XSparks platform · sample workspace data
- 04 · Govern
You stay in charge.
You set the dollar amount. Nothing above it moves without a person saying yes. And any piece of it stops the second you want it to. Fourteen waiting on a human. One hundred and forty-seven cleared as low risk.
Spend limits, a human queue, and an audit trail on every run.
Dollar limits by company, by department and by model provider, enforced at the router, with alerts before a limit and an automatic fallback provider at it. Work that needs a person lands in a review queue: what is pending, what was approved, what cleared as low risk, approve or reject on the record itself. SSO and SAML, with role-based access down to the individual workflow, agent and context. Every run logs who triggered it, which model, which provider, which inputs, which outputs.
Live XSparks platform · sample workspace data
+38 contexts · pricing rules v3 · versioned, roll back in one clickRouted to the cheapest model that passes its eval · $0.31 / runSales · run started · Quote build from spec sheet · 4 systems
INV-1048 approved by J. Morales · 14 pending → 13Every workflow.What it costs.What it returns.Where it stops.
One screen: what each piece of work costs down to the token, what it put back, who's using it, what's waiting on approval. If you can't state your AI return in dollars today, this is what's been missing.Per-run cost attributed to workflow, team and user automatically. Nobody tags anything. Read-only for finance. Exports to CSV. Your data, in your tenant.
The ROI Dashboard is the XSparks screen that shows what every workflow costs, what it returns, and what still needs a human's approval.
Built for finance. Clear enough for the board. See the full ROI Dashboard →
| Workflow | Status | Runs / mo | Model cost | Hours off desk |
|---|---|---|---|---|
| Renewal desk, tier 2 accounts | Live | 1,284 | $412 | 96 |
| Quote build from spec sheet | Live | 640 | $1,109 | 143 |
| Field ticket to invoice | Live | 2,970 | $338 | 61 |
| Vendor onboarding pack | Awaiting approval | 0 | $0 | 0 |
- Agents built for your business. Sales follow-up, call handling, reporting, finance ops. Built, not bought off the shelf.Per-run cost accounting. Model, provider, task, workflow and user on every run, rolled up to team and company with nobody tagging anything.
- Cost and usage by team. See exactly who's using what and what it costs, down to the token.Return in your finance team's terms. Saved hours, captured dollars and avoided rework, computed from the method your finance team signed before the build.
- Return in dollars, not guesses. Every agent's output tied to a number your CFO will accept.Your own audit trail. Who triggered what, on which model, with which inputs and outputs, pulled whenever you want it.
- Humans on every decision that matters. Approval gates engineered in, not bolted on. AI never runs unsupervised where it counts.Export everything. Runs, costs and definitions to CSV. Any other connector we build if you need it.
Seen the ROI Dashboard. Now bring us the workflow that costs you the most.
Where the outcome can be measured, part of our fee sits on it.
For work where the baseline and outcome can be measured together, part of our fee sits on the outcome. Agreed up front, capped both ways, trued up quarterly. Ask any AI vendor what happens to their invoice when the outcome misses. Then ask us.The outcome share is defined in the SOW: the metric, the source tables, the exclusions and the cap, in both directions. If a baseline can't be measured from your systems, that workflow runs on a flat fee instead. No blended numbers.
- 1.1The baseline is yours, before we touch anything.
Trailing twelve months, agreed in writing in week one. A baseline set after we start means nothing.
Baseline extracted read-only from your ERP and CRM.Trailing twelve months, pulled by query with your finance team watching, signed as an appendix to the SOW.
- 1.2Your finance team owns the definitions.
They decide what counts as a saved hour or a captured dollar, and sign the method before anything ships.
The measurement method is a written spec.Source tables, definitions, exclusions and review cadence. Your finance team edits it before anything ships and can rerun it without us.
- 1.3The six numbers, every ninety days.
Not an activity report. The same review shown on the right, for every workflow, signed by your own finance team.
The quarterly true-up runs from the same spec.Same query, same definitions, ninety days later. The fee share is capped in both directions and stated in the SOW.
- 1.4One primary outcome per workflow. Nothing counted twice.
Hours back, cost down and capacity up can be the same benefit three times. Each workflow gets one finance-agreed primary outcome and at most two supporting indicators.
One primary metric, up to two supporting, no overlap.Finance signs the formula, source tables, exclusions and review cadence before the build. The same benefit is never counted under two labels.
- 1.5You own your data, your prompts and whatever you built.
Data, logs, and the agents and prompts your team configured leave with you in full. The platform is licensed, so our code and the workflows we ship on day one stay ours. Nothing of yours is locked to us.
| Number | Baseline · TTM | This quarter | Signed by |
|---|---|---|---|
| ↓Cost down | $48.2k / mo | $41.9k / mo | Client finance |
| ↑Revenue up | $1.20M pipeline | $1.34M | Client finance |
| ↑Hours back | not tracked | 300 hrs | Ops lead |
| ↑Capacity | 14 quotes / wk | 41 quotes / wk | Ops lead |
| ↑Quality up | 6.1% rework | 1.8% | Client finance |
| ↓Risk down | 0 gated | 14 gated · 147 auto | Owner |
We're not consultants.We ran the companies.
Every firm in this market will tell you it has experience. Ask what they built. We were operators long before any of this was called AI, and the problem above is one we had in our own business first.
What every published XSparks result must include.
A number without these five things is marketing. When we publish a result, all five come with it, or it doesn't get published.
- 1Company, or approved contextNamed, or described in terms the client signed off.
- 2BaselineTrailing twelve months, agreed before we start.
- 3Date rangeWhen it was measured, start to end.
- 4Measurement methodHow the number was counted, in writing.
- 5Client finance sign-offTheir finance team's signature on the number.
33+ years in enterprise IT and AI transformation. Founder and CEO of Mindmatrix. Formerly EY and IBM.
AI researcher and founder of Nervesparks. Builds safety-first AI for defense, healthcare and energy.
30 years in enterprise software and partner ecosystems. Owns the quarterly review and the six numbers.
Founder who has built and run his own companies. Leads go-to-market and the live workshops where owners learn to put AI to work.
Find out where you actually stand.
12 questions. Under 3 minutes. It covers what actually decides whether AI works here: documentation, process, knowledge, adoption, measurement, oversight, approvals, carry-over. Your score on screen. No deck. No sales call unless you ask for one.
Your weakest dimension is your real level, and that's usually the part that stings a little. The readout is written for your seat and your specific gap, not some generic report you'll skim once and forget.
Or skip it · bring us one stuck workflow · hello@xsparks.ai · or train your team on Claude, live with Nav