AI for finance and analytics, built in layers.
Strata Intelligence is the fractional AI office for finance and analytics teams at 20–200 person companies. One-week diagnostic. Monthly partnership. Production workflows shipped — not strategy decks.
AI advantage is an operations problem — not a technology problem.
The models are ready. The infrastructure is ready. The use cases are obvious. What separates finance and analytics teams pulling ahead with AI from teams still "exploring it" is not technology choice. It is operational discipline — the ability to scope tight, ship fast, and own what gets built. Strata exists to compress that timeline for the data-and-numbers teams most likely to win.
Working software, not strategy decks.
Every dollar spent on a slide that doesn't ship is a dollar a competitor is spending on production. Strata measures success in deployed workflows — variance commentary that drafts itself, forecasts that explain themselves, knowledge bases your analysts actually use.
Productized over bespoke.
Most AI value for finance and analytics is locked behind unglamorous, repeatable work. Six-month bespoke transformations are theatre. Six-week productized builds are systems. We treat AI like infrastructure — boring, owned, and always on.
Speed compounds. Ceremony doesn't.
A finance team that ships its first AI-assisted variance write-up in week three is learning by week four. A team still in discovery at week ten is behind, and accelerating in the wrong direction. The cost of a slow start is paid in every close cycle that follows.
Built to be outgrown.
The best fractional AI partner is one whose work outlives the engagement. Strata hands over everything — code, prompts, configs, eval harnesses, runbooks. No vendor lock-in. No consulting moat. If you stop working with us tomorrow, your AI keeps running.
Your team is already doing this work — just without leverage.
Most finance and analytics leaders know exactly where AI should fit. The blocker isn't strategy. It's that nobody on the team has the time, mandate, or pattern library to ship it. The cost shows up every close cycle.
Close cycle eats weekends.
Variance commentary, board narrative, executive summary — they're all manual, they all hit the same week, and they all land on your most senior people. Every quarter. The "we'll automate this someday" backlog never moves.
The ad-hoc queue never ends.
Your analysts spend more time answering "why did this number move?" than running the analysis that should drive next quarter's plan. The dashboard exists. The explanation is locked in a Slack thread.
AI licenses, no playbook.
Everyone on the team is using ChatGPT for their own one-offs. Nobody is building anything that compounds. You know there's leverage here, but no one is driving it — and a full-time Head of AI is six months and $250k away.
Lean finance and analytics teams that need to ship.
Strata is purpose-built for the data-and-numbers function at 20–200 person B2B companies — the teams where the cost of *not* operationalizing AI shows up every close cycle, every board prep, and every ad-hoc executive request. We work best with leaders who can swipe a card without a procurement committee.
Lean FP&A & Finance Teams
Finance leaders running close cycles, board prep, and variance analysis on too little headcount — and an internal data team that's busy on product, not on you. Strata helps your team draft commentary, scale forecasting workflows, and stop spending Sunday nights on Monday's deck.
Analytics & Data Teams Drowning in Asks
Heads of Data and analytics teams underwater on ad-hoc executive requests, dashboard interpretation, and stakeholder Q&A. Strata builds copilots that absorb the long tail so your team can focus on the analyses that actually matter.
Founder-Led B2B Without an AI Lead
Founders, COOs, and CFOs at growth-stage B2B companies who know AI matters but have no internal champion to drive it. Strata is the fractional partner that turns "we should be doing more with AI" into a 90-day plan and shipped workflows.
Two layers that matter. Workshops for teams not yet ready.
Strata is built around a deliberate two-layer model. Every engagement starts with a one-week diagnostic. From there, the value compounds inside a monthly partnership. The workshop exists as a lower-commitment first touch — but it is not where the work lives.
After a Strata partnership, the work changes.
Strata sells engagements. What finance and analytics teams keep is a different operating posture — workflows that scale, knowledge that's searchable, decisions that are faster, analyses that draft themselves.
Variance commentary drafts itself. The first pass of MoM/QoQ explainers is generated from your actuals + drivers; your team edits the narrative instead of authoring from scratch.
Forecast cycles compress. Scenario modeling, sensitivity analysis, and forecast commentary that took two days now run in two hours — with the analyst in the loop, not bypassed.
Board prep stops eating weekends. Deck drafting, KPI commentary, and investor updates pull from your systems automatically. Leadership edits the narrative; analysts get their lives back.
Institutional knowledge becomes searchable. KPI definitions, prior board decks, policy memos, and past analyses all answer questions in natural language — with citations back to source.
Dashboards explain themselves. "Why did margin compress in EMEA last week?" becomes a one-click answer, not a Slack thread that ties up two analysts for a half-day.
The AI question stops being a question. The team stops asking "should we be using AI for this?" and starts asking "what should we ship next?" — with a roadmap to point at.
Three offers. No custom scoping required.
Every Strata service has a fixed scope, a fixed price, and a clear purpose. The audit is the front door. The fractional partnership is the main offer. The workshop is the warm-up. Builds — copilots, RAG, automation — happen inside the partnership, not as separate SKUs.
AI Opportunity Audit
A structured one-week investigation into your finance or analytics function. Strata maps your workflows, scores AI opportunities by impact and effort, and delivers a 90-day roadmap. Often the only engagement a client needs to unlock $250k+ in annual time-savings — and the highest-converting entry point into a fractional partnership.
- Kickoff call with leadership to align on goals and constraints
- 3–5 interviews with operators across finance and analytics
- Workflow map of current close cycle, planning cycle, and reporting cadence
- Catalog of 5–10 specific AI use cases applicable to your team
- Impact × effort scoring with estimated annual time-savings per use case
- 90-day implementation roadmap with sequencing recommendations
- Tailored prompt library — 5–10 prompts deployed in a Claude Project (or your preferred LLM platform), built around the workflows we mapped — for your team to use immediately
- 1-hour readout call with leadership + recorded summary
- Optional: written proposal for monthly partnership engagement
Fractional AI Partner
The main Strata engagement. A monthly partnership where Strata is embedded as your fractional AI office — shipping workflows, training your team, advising on tools, building the next quarter's roadmap. Copilots, RAG systems, and automations are built here, inside the partnership, not as separate projects.
You get a senior AI partner on call without the cost of a full-time hire (~$200–300k/year), the recruiting cycle, or the equity ask. Most clients sign up after a successful audit.
- Shared Slack channel — async access during business hours, 4-hour median response
- Biweekly 1-hour working session with leadership or the AI champion
- Hands-on builds: variance commentary, forecast copilots, KPI RAG, dashboard explanation, board-prep workflows
- Quarterly roadmap reviews — what to build next, what to retire
- First-look briefings on new tools, models, and patterns relevant to finance/analytics
- Vendor evaluation when you're considering AI products (Looker AI, Anaplan AI, etc.)
- Eval support — measure whether what's been built is actually working
- All builds delivered to your repository; client retains 100% IP
AI Workshop for Finance & Analytics
A half-day intensive that turns "we have AI licenses but nobody uses them" into a team that actually uses AI in close cycles, planning workflows, and ad-hoc analysis. Role-specific prompt libraries, hands-on practice, and take-home cheat sheets tuned to finance and analytics work.
Most often used as a lower-commitment entry point for teams not yet ready for the audit — a way to see how Strata works before committing.
- Pre-work intake — current tools, roles, pain points
- 3.5 hours on-site or remote, up to 25 attendees
- Hands-on practice with Claude, ChatGPT, and Cursor — not slide-only
- Role-specific prompt libraries: variance write-ups, forecast commentary, SQL generation, dashboard explanation
- Common-pattern walkthroughs: financial modeling assistance, board-prep drafting, ad-hoc analysis acceleration
- Anti-patterns review — when not to use AI in finance work, common failure modes
- Take-home cheat sheet and prompt pack per role
- 30-day follow-up office hour for stuck-points
What Strata builds inside the partnership.
The list below is a non-exhaustive selection of the AI systems Strata builds for finance and analytics teams. Every Embed engagement picks from this library — patterns reused across clients ship faster and cleaner than bespoke work.
01 Reporting & Variance Analysis
Compress the close cycle. Turn raw actuals into executive-ready commentary on day one.
- Variance commentary drafting. Generate first-pass MoM/QoQ explanations grounded in your actuals + drivers + prior commentary, ready for analyst review.
- Executive narrative synthesis. Compile finance, ops, and customer signals into a single coherent story for leadership.
- Reporting-package automation. Standardized monthly/quarterly packages that draft themselves from system data with consistent voice and structure.
02 Forecasting & Planning
Faster scenarios. Better assumptions. Forecasts that explain themselves.
- Scenario modeling assistant. Generate and stress-test scenarios against your driver model with explicit assumption documentation.
- Forecast commentary copilot. Draft the narrative around forecast changes, sensitivity analysis, and quarter-end re-baselines.
- Budget vs. actual explainer. Auto-explain budget variances with root-cause hypotheses and recommended next steps.
03 Board & Investor Prep
The cycle that eats weekends — collapsed into hours of editing instead of days of authoring.
- Board deck drafting. Pull metrics from your systems, draft the narrative slide-by-slide, leadership edits the framing.
- Investor update generation. Monthly/quarterly investor letters and update emails drafted from real numbers and recent context.
- KPI commentary at scale. Every KPI on the deck has a one-paragraph explainer; no analyst-time spent on the boilerplate.
04 Internal Knowledge & Documentation
Searchable institutional knowledge — KPI definitions, prior decks, policies, past analyses.
- KPI glossary RAG. Natural-language search over KPI definitions, source-of-truth dashboards, and historical drift in calculation methodology.
- Prior-analysis search. "What did we conclude about EMEA margin in Q2 2024?" — answered in seconds with citations to the original deck.
- Policy & assumption knowledge base. Internal finance policies, planning assumptions, and historical decisions — searchable, citable, current.
05 Analysis Acceleration
Free your analysts from the long tail of ad-hoc requests.
- SQL & query generation. Natural-language → tested SQL/Python against your data model, with guardrails on production tables.
- Dashboard explanation. "Why did this number move?" answered from the underlying data + change context, not from a Slack thread.
- Anomaly investigation. Watch metrics, flag shifts, and propose root-cause hypotheses for analyst review.
06 Operational Workflows
The recurring manual work that eats finance and analytics headcount.
- Reconciliation copilots. Match invoices, expenses, ledger entries; flag exceptions for human review with context.
- Meeting → action plan. Finance review transcripts → structured action items, owners, and follow-up tracking.
- Ad-hoc data request triage. Inbound exec requests classified, scoped, and drafted before they hit your analysts' queue.
What a Strata-built AI workflow looks like.
Most "AI projects" in finance and analytics fail because someone wired ChatGPT to a Zapier flow and called it a system. Real production AI has five layers, every one of which can quietly break. Below: how Strata builds, and how Strata measures.
Citation-grounded answers
If the model can't justify a claim with a source from your data — a ledger entry, a deck citation, a policy reference — it doesn't make it. No silent fabrication.
Eval harness from day one
Held-out test cases, automated quality scoring, and a kill switch. Numbers visible before launch and watched in production. Numerical accuracy matters in finance — we measure it.
Vendor-neutral by design
Claude or GPT, Pinecone or pgvector, Slack or web app. Choices are made on your data, your stack, and your security posture — not on any partnership or reseller arrangement.
How an engagement actually goes.
Strata does not sell six-month transformations. Every engagement is structured around weeks of focused work that compound into an ongoing partnership. Four steps from first email to embedded.
30-minute call
No deck, no pitch. We ask questions to figure out whether the audit is the right starting point — or whether to start with a workshop or skip to a partnership conversation. You leave knowing whether Strata can help.
One-page SOW
Deliverables, dates, acceptance criteria, single number. Mutual NDA if needed. Sign electronically. Work begins the following week.
Audit & readout
Discovery interviews, workflow mapping, use-case scoring, 90-day roadmap. Live readout call with leadership. Most clients sign on for the partnership within 30 days.
Embedded partnership
Shared Slack, biweekly working sessions, hands-on builds against the roadmap. Working software every two weeks, not at the end. Quarterly roadmap reviews. Cancel any month.
How Strata works.
Six operating principles that define every engagement. Designed so the cost, scope, and outcome are knowable before the second meeting — and so what gets built belongs to the client when the work is done.
Fixed scope, fixed price.
Every offer has a number on it. Every SOW has acceptance criteria. No hourly billing. No change orders. The total cost of an engagement is knowable before signature.
Working software every two weeks.
Partnership engagements deliver shipped artifacts on a biweekly cadence — not a final unveil. Progress is visible. Direction can be corrected. No surprise at handoff.
Vendor-neutral by design.
No reseller deals. No model lock-in. Strata chooses Claude, GPT, or open weights based on your data, your stack, and your constraints — and documents why.
You own everything we build.
Code, prompts, configs, runbooks, evals — delivered to your repository. No vendor moat. No consulting moat. If the partnership ends tomorrow, your AI keeps running.
Evals from day one.
Every build includes an eval harness. Numerical accuracy matters in finance work. Numbers visible before launch and watched in production. If a system drifts, the alert fires before close cycle does.
Embedded, not extractive.
Partnerships end when the system is owned by your team — not when the contract expires. Strata succeeds when clients no longer need a consultant to keep going.
Strata vs. the alternatives.
Finance and analytics leaders evaluating AI capacity typically consider three alternatives — and miss the fractional model Strata is built around. Here is how the options actually stack on the dimensions that decide the engagement.
| Big 4 Consultancy | Full-Time Head of AI | Generic AI Agency | Strata (Fractional) | |
|---|---|---|---|---|
| Time to value | 6–12 months | 6+ months to hire, then ramp | Variable, scope-creep prone | 1 week to roadmap, ongoing from there |
| Annual cost | $200k+ minimum | $200k–$300k + benefits + ramp | Hourly, change orders, lumpy | $60k–$180k all-in for partnership |
| Primary deliverable | Strategy deck | Eventually, code + judgment | Whatever was scoped | Shipped workflows + embedded partner |
| Finance/analytics fluency | Generalist with AI practice | Depends entirely on the hire | Generic — sales/marketing flavor | Specialist; library tuned to function |
| IP ownership | Often shared or vendor-tied | Yes | Sometimes, depends on contract | 100% client |
| Vendor neutrality | Partnership-driven | Whatever the hire prefers | Tool-of-the-month | Chosen per engagement |
| Exit cost | High — retainer dependencies | Replacement risk + severance | Hand-off quality varies | Zero — cancel any month, IP yours |
Working knowledge of the stack — without being married to it.
The questions every client asks first.
If yours is not below, send a note. The list grows with the questions Strata gets asked most often.
Q1How can Strata ship this fast?
Strata uses AI-native tooling — Claude Code, Cursor, agentic patterns — to do the implementation work, which means delivery time goes into the parts that actually require senior judgment: scoping, architecture, evals, and integration. A productized library of finance/analytics workflow patterns means each new engagement reuses what worked for the last client, not starting from scratch.
Q2What if we don't know what we need?
That is exactly what the audit is for. $3,500, one week, a ranked list of where AI fits and where it doesn't in your specific finance or analytics workflows. If the list isn't useful at the end, you've paid less than a small SaaS subscription to find out. Strata will say so directly if AI isn't the right move right now.
Q3Who owns what gets built?
The client owns everything. Code, prompts, configs, evals, documentation — delivered to your repository. Strata retains the right to reuse anonymized patterns (architectures, prompt structures) in other work, but nothing identifiable to your business.
Q4Can Strata work with our existing finance stack?
Yes. NetSuite, Sage, Workday, Anaplan, Pigment, Adaptive, Cube, Mosaic, Looker, Tableau, Snowflake, BigQuery, Excel/Sheets — Strata builds on top of what your team already lives in. Vendor-neutral architecture is a founding principle.
Q5What if a system gets a number wrong?
Numerical accuracy matters in finance work. Every Strata build includes an eval harness with held-out test cases and quality scoring before launch. Anything generating numbers includes citations to source so an analyst can verify. If a system drifts, monitoring fires before close cycle does. Models do not get unsupervised authority over the books.
Q6What about data security and compliance?
Strata works in your infrastructure or with providers you already trust (Anthropic enterprise, OpenAI enterprise, etc.). Mutual NDA standard. DPAs and BAAs available. Financial data is treated with explicit scope, controls, and documented data flow. SOC 2 aligned process available for clients who require it.
Q7How quickly can we start?
An audit typically begins 5 business days from signature. Partnership engagements start 1–2 weeks from signature, depending on data access provisioning. Strata does not take on more clients than it can ship for — if the timeline is two weeks, the timeline is two weeks.
Q8Why fractional instead of a full-time hire?
A senior Head of AI runs $200–300k/year fully loaded, takes 6+ months to hire, and brings one perspective. The Strata partnership is $60–180k/year all-in, starts in two weeks, and brings cross-client pattern library and senior judgment from the first week. When you reach the scale where a full-time hire makes sense, Strata is the partner that helps you scope and recruit them.
One week. $3,500. A ranked plan.
The AI Opportunity Audit is the lowest-risk way to find out exactly where AI moves the needle in your finance or analytics function — and where it doesn't. Pick a time, answer three quick questions about your team, and Strata arrives prepared. No deck, no pitch — just an honest conversation about whether the audit fits.