Executive Summary
Una is a Toronto-based, AI-native FP&A and "Performance Planning" platform launched in December 2024 by the most credentialed founding team in the category: Don Mal (co-founder of Vena Solutions, which he scaled past $100M ARR, and Fluence Technologies, acquired by Anaplan in 2024) as Executive Chairman, with Fluence's former CEO Michael Morrison taking over as Una's CEO in February 2026. The company has raised approximately US$13M in seed funding led by Staircase Ventures and positions itself as "the FP&A platform built for the AI era" — a deliberate rebuild of financial planning around what AI can do when it is designed in from day one rather than bolted on.
Three ideas define the product. First, an agentic AI architecture: a Supervisor agent orchestrating Planner, Analyst and Modeler agents on top of an AI Foundation layer for anomaly detection, predictive forecasting and natural-language queries — including MCP support that lets external AI assistants like Claude interact with the model. Second, revenue intelligence inside the planning engine: pipeline, bookings and churn from Salesforce or HubSpot feed the forecast directly, rather than living in a separate RevOps tool. Third, a both/and Excel strategy: UnaXL projects the governed cloud model into Excel, echoing the founders' Vena heritage without making the spreadsheet the engine.
The honest counterweight: Una is very young. It launched publicly less than two years ago, discloses no customer count or ARR, has essentially one public G2 review, and sits in the entry-level "Core Provider" quadrant of Nucleus Research's 2026 CPM Value Matrix — even as the same firm named it a "Hot Company to Watch in 2026" and BPM Partners named it Best New Vendor of 2025. Named customers (UpKeep, Northspyre, 4AG Robotics, SafetyChain) show real outcomes, but the evidence base is thin relative to Abacum, Cube or Pigment. This is a trajectory buy, not a proof buy.
Una is the most interesting early-stage bet in Gen-3 FP&A: category-veteran founders, genuinely AI-native architecture, and a revenue-intelligence thesis no direct competitor centers. Choose it if you are a mid-market or PE-backed finance team comfortable being an early customer — and use that position ruthlessly: design-partner pricing, reference-for-discount trades, multi-year price caps and change-of-control protections. Do not choose it if you need a deep reference base, close/consolidation of record, or enterprise-scale proof today — Abacum, Cube, Vena or Pigment are the safer picks. Re-evaluate in 12 months: if the CPM expansion and UnaXL mature on schedule, the risk calculus changes materially.
Company & Product Snapshot
Who Should Evaluate Una
- Mid-market finance teams ($20M–$500M revenue) outgrowing spreadsheet planning — especially multi-sheet or monolithic-workbook setups
- Growth-stage SaaS and recurring-revenue companies wanting pipeline, bookings and churn wired directly into the forecast
- PE-backed operators needing driver-based models that survive add-on acquisitions and new dimensions mid-year
- Lean FP&A teams (1–5 people) that want AI agents doing model-building and analysis legwork
- Teams comfortable as early design partners, trading vendor youth for pricing leverage and roadmap influence
- Organizations needing financial close, statutory consolidation or intercompany eliminations of record — out of scope
- Large enterprises requiring Anaplan/Pigment-scale dimensionality with a proven track record
- Risk-averse buyers requiring a deep reference base and years of production evidence
- Google-Workspace-first teams — the spreadsheet path (UnaXL) is Excel; no Sheets equivalent is published
- Teams whose spreadsheet model is sound and who only need data plumbing — Cube or Aleph is the lighter fix
Product Capabilities & Strengths
Capability Scorecard
Core FP&A
72/100
Financial Close & Consolidation
15/100
Reporting & Analytics
68/100
AI Innovation
85/100
Ease of Use
80/100
Implementation Speed
85/100
Data Integration
70/100
Scalability & Maturity
52/100
Driver-based budgeting and rolling forecasts on an "elastic data engine" that recalculates when assumptions change and restructures as you add products, regions or entities. Continuous reforecasting is the design center rather than annual budget cycles — customer evidence backs it (UpKeep's monthly reforecast runs in about an hour). Scenario modeling with side-by-side comparison. Automatic multi-entity aggregation for planning purposes, demonstrated across three countries at 4AG Robotics. This is competent Gen-3 core planning; the differentiation lives in what feeds it and what operates on it.
Una's structural bet, and the capability Nucleus Research singled out: go-to-market signals — pipeline coverage, bookings velocity, churn and expansion from Salesforce or HubSpot — are inputs to the planning engine itself, not a dashboard beside it. Revenue models adapt as deal data changes, and the AI forecasting layer is built to produce usable predictions even with incomplete data. For recurring-revenue businesses where the revenue plan IS the plan, this collapses the FP&A-to-RevOps handoff that most competitors leave as a manual export.
Position-level headcount planning connected to the expense model — hires, attrition, merit cycles and loaded costs flow through to opex and cash. Case-study evidence (UpKeep consolidating headcount planning out of standalone sheets) suggests it handles the standard mid-market use case; deep skills-based or capacity-planning workflows found in dedicated workforce tools are not the focus.
Embedded BI-style dashboards and visualizations with variance analysis against plan, praised in early user feedback for ease of building. Board and management reporting is functional but not the narrative-reporting depth of mature CPM suites. The unusual piece is the Action Tracker ("Performance Driver"): planning decisions become tracked actions with owners and status — an execution-accountability layer that is near-unique in the category and hints at the platform's "performance planning" framing.
Full platform functionality surfaced inside Excel: budget owners work in the grid they know while the model, security and single source of truth stay governed in the cloud. Strategically this is the Vena playbook rebuilt on a modern engine — "keep Excel or leave it, your choice." Caveat: UnaXL is the newest, least-publicly-proven module (it appears on the platform page but has no launch coverage or customer evidence yet). Treat it as promising and demo it hard rather than assuming Vena-grade maturity.
Una is the only Gen-3 FP&A platform that combines a from-scratch agentic AI architecture with revenue intelligence wired directly into the planning engine — built by founders who already built and exited two CPM companies. Nobody else in the mid-market set centers both. The trade-off is maturity: every advantage above is real but young, and the proof base is a fraction of what Abacum, Cube or Pigment can show.
Architecture & Technical Foundation
Una is cloud-only SaaS built around a driver-based modeling engine the company calls an "elastic data engine" — models restructure as dimensions are added rather than being rebuilt, and assumption changes propagate immediately. The AI layer is architectural, not appended: agents operate on the model natively, and the platform exposes MCP so external AI assistants can interact with governed planning data. Sign-in runs through a dedicated portal (portal.unasoft.app); no on-premise or hybrid options exist.
Driver-based cloud modeling core; when an assumption changes the model updates, and when the business adds products, regions or entities the model structure evolves without a rebuild — the pitch aimed squarely at PE-backed operators doing add-ons
Supervisor agent orchestrating specialized Planner, Analyst and Modeler agents, plus Query, Discovery, Navigation and Document assistants — launched May 2025 as a from-scratch architecture rather than a copilot retrofit
Anomaly detection, clustering, regression and predictive time-series modeling underneath the agents; NLP query across the model; forecasting designed to remain usable with incomplete data
Model Context Protocol endpoint allowing external AI assistants (Claude, ChatGPT and similar) to interact with the planning model — confirmed in production use by an early customer, and ahead of the mid-market category
Multi-entity, multi-currency aggregation for planning and reporting purposes — proven across three countries at 4AG Robotics — explicitly NOT statutory consolidation or close-of-record
Una deliberately excludes financial close, statutory consolidation, intercompany eliminations and certification workflow — territory this exact team covered at Fluence before selling it to Anaplan. The February 2026 funding announcement flags expansion "into CPM," so watch the roadmap; today, pair Una with your ERP close process or a dedicated close platform.
No public evidence yet of Una running enterprise-scale models (thousands of users, massive dimensionality). The named customer base is mid-market. The architecture may well scale — but if you need proven scale today, that is Pigment or Anaplan territory. Nucleus's "Core Provider" placement reflects exactly this maturity gap.
AI & Intelligent Planning Capabilities
AI is Una's reason to exist, not a feature checkbox — the founding narrative is explicitly "rebuild FP&A around what AI does best, from day one." The portfolio launched in May 2025 and spans agentic workflows, predictive forecasting and natural-language interaction. It is the most architecturally ambitious AI story in mid-market FP&A; it is also barely a year old in production, with no independent accuracy benchmarks. Both things are true.
Ingests raw files and existing spreadsheets and generates a structured, driver-based financial model — the 'model in minutes' claim. The highest-leverage agent if it works on your real files; test it on your messiest workbook, not their sample
Orchestrated by a Supervisor agent: scenario construction, variance analysis, driver investigation and narrative explanation of what changed and why — positioned as legwork elimination for lean FP&A teams
AI Foundation time-series models (regression, clustering, anomaly detection) that adapt forecasts to revenue signals — pipeline, bookings, churn — and are designed to produce usable output even with incomplete history
Natural-language interface across the model and its documentation — ask questions of the plan in plain English rather than navigating cube views
Expose the governed model to your own AI assistants; an early customer publicly cites using Claude against Una via MCP. Ask hard governance questions — permissions, audit, read/write boundaries — before enabling it broadly
Architecture: ahead of the category — genuinely agent-native where most competitors retrofit copilots onto Gen-2 engines. Evidence: thin — launched May 2025, one public customer confirmation of the MCP workflow, no independent forecast-accuracy benchmarks. Evaluation guidance: score every AI claim on your own data in the demo (see Demo Questions below), get agent outputs that reconcile to the ledger, and treat "AI does the modeling" as a hypothesis you are testing, not a feature you are buying. If the demo performs, this is the strongest AI story in the mid-market set; if it stumbles, the rest of the platform still has to win on Gen-3 fundamentals.
Integration Ecosystem
Una claims 100+ integrations spanning ERP and accounting, CRM, HRIS, billing, BI, data lakes and warehouses, file storage and Office — with bi-directional sync in real-time, batch or manual-push modes. The independently verifiable core is narrower: NetSuite, Salesforce and HubSpot are the named, customer-proven connectors, and NetSuite is the flagship (4AG Robotics runs automated actuals across three countries on it). The honest read for buyers: the connectors that match Una's ICP are real and proven; the long tail of the "100+" claim should be verified against your specific stack, in writing, during evaluation.
The full connector catalog is not published — only NetSuite, Salesforce and HubSpot are verifiably named, and maturity labels beyond those reflect vendor claims. During evaluation, get the named connector list for your exact systems with sync direction and refresh frequency for each, and treat "CSV import" as a fallback, not an integration. Google Sheets deserves specific attention: several case-study customers came OFF Sheets-based planning, but no Sheets connector is published.
Implementation & Deployment Timeline
Una advertises a 4-week implementation with AI-assisted onboarding: the Modeler agent builds the initial model from your existing files, and a Global Template Library (an investment focus of the 2026 funding round) supplies industry-standard planning structures. Customer evidence is directionally supportive — 4AG Robotics reports going live "in weeks" — though the 4-week figure is vendor-claimed and there is no SI or partner ecosystem yet, so delivery capacity is Una's own team. For comparison: this is Pigment/Abacum-class speed (2–4 months typical) compressed further, and a different universe from enterprise EPM (6–12 months).
Discovery & Data Connection
Week 1- Kickoff with Una's onboarding team, connect actuals sources (NetSuite, accounting system, CRM), map chart of accounts and revenue data feeds
AI-Assisted Model Build
Weeks 1–2- Modeler agent ingests existing spreadsheets and raw files to generate a structured driver-based model; Global Template Library applied for industry-standard planning structures; headcount, expense and revenue models configured
Validation & Parallel Reforecast
Weeks 2–3- Reconcile model outputs to actuals and your legacy spreadsheet model, validate driver logic and scenario behavior, run a parallel reforecast cycle before cutting over
Training & Go-Live
Weeks 3–4- Finance team training on agents, dashboards and UnaXL, budget-owner rollout, cutover from legacy spreadsheets, first live rolling reforecast
- Modeler agent converts existing spreadsheets into structured models — the slowest part of any FP&A implementation, compressed
- Vendor-guided onboarding with no SI required (or available) at mid-market scale
- Parallel-run validation against your legacy model fits inside a single reforecast cycle
- Template library reduces blank-canvas time for standard SaaS and industrial planning structures
- Watchout: a small vendor team means implementation capacity is finite — confirm your start date and named onboarding resources contractually
Pricing & Total Cost of Ownership
Una publishes no pricing, offers no free trial, and — unusually even for this category — no third-party benchmark data exists yet on Vendr, Capterra or elsewhere. Every deal is negotiated. Anchor your expectations to mid-market Gen-3 FP&A economics (roughly $25K–$75K/year depending on seats, modules and company size) and then use Una's stage against the list price: a seed-stage vendor building its reference base has every incentive to discount for the right logo.
Quote-based only. Expect per-seat or tiered platform pricing typical of the category. Insist on a written quote broken out by platform fee, seats (contributor vs. viewer), modules (UnaXL, AI features) and implementation — bundled "one number" quotes hide the levers you can negotiate.
Typical mid-market Gen-3 FP&A range (Cube, Abacum, Drivetrain deployments). Una should land at or below comparable quotes given its stage — if a Una quote comes in above an Abacum or Drivetrain quote for the same scope, the maturity math doesn't support it. Get competing quotes; this category discounts against each other readily.
Your leverage is different with a seed-stage vendor than with Workday — use the right levers: (1) Reference value: offer a named case study, G2 review or reference calls in exchange for a meaningful discount — Una's review base is nearly empty and each public logo is worth real money to them. (2) Multi-year price caps, not multi-year prepay: lock renewal increases at 3–5% now (a Series A typically brings pricing discipline), but pay annually — never prepay years to a pre-Series-A company.
(3) Change-of-control protections: both founder companies exited (Vena to PE, Fluence to Anaplan); negotiate contract language preserving pricing and support if Una is acquired. (4) Exit rights: data-and-model export assistance plus a termination right if roadmap commitments (UnaXL maturity, CPM expansion) slip past agreed dates. (5) Implementation: push for onboarding included — with no SI channel, implementation is their cost of sale, not yours.
Customer Case Studies & Outcomes
Una's public customer evidence is young but specific — real company names with quantified outcomes, which counts for more than volume. The pattern across all of them: consolidation of fragmented spreadsheet planning (multiple Google Sheets, monolithic Excel workbooks) into one connected driver-based model, with reforecasting time as the headline metric. No disclosed customer count or ARR exists to size the base beyond these.
Planning fragmented across five separate Google Sheets models covering headcount, expenses, revenue and cash — every reforecast meant manual reconciliation across all of them
Consolidated all five spreadsheets into a single connected Una model spanning headcount, expenses, revenue and cash
A lean finance team maintaining a 150MB Excel planning model that was slow to open, fragile to change and consumed days of forecasting effort each month
Retired the monolithic Excel model entirely and moved forecasting into Una's driver-based engine
Quarterly planning required multi-day spreadsheet rebuilds across operations in three countries with no automated connection to NetSuite actuals
One unified planning model running on automated NetSuite actuals across all three countries
Needed planning connected to go-to-market reality — pipeline, bookings and churn — rather than a finance-only budgeting tool
Selected Una at launch for its combined revenue-intelligence-plus-planning vision; CFO Drew Stovall publicly endorsed the platform direction
Go-to-Market & Support Model
Una sells direct, with a founder-heavy motion typical of its stage — expect senior people (including the leadership bench) in your deal. The February 2026 round explicitly funds sales, marketing and partnership expansion, and the CEO change to Michael Morrison signals a scaling phase. There is no SI or implementation-partner ecosystem yet: onboarding, support and success all run through Una's own team, which cuts both ways — high-touch attention now, unproven capacity as the base grows.
CFOs and heads of FP&A at mid-market companies — growth-stage SaaS, recurring-revenue businesses and multi-entity industrials — plus PE operating partners standardizing planning across portfolio companies
Explicitly hunting legacy replacements: the company cites that roughly half of FP&A deployments today replace existing software — Vena, Adaptive and Prophix installs are the stated targets, a pointed move given the founders built one of them
Gartner CFO Conference exhibitor (2025), BPM Partners Best New Vendor 2025, Nucleus Hot Company to Watch 2026 — an aggressive analyst-relations motion for a seed-stage company
Staircase Ventures (Janet Bannister, lead), Emerald Development Managers; ~US$13M total seed. No PE ownership dynamics — but also no deep war chest relative to Series-B-funded competitors
Vendor-direct onboarding and success; no published SLAs or support tiers. Negotiate named resources and response commitments into the contract rather than assuming enterprise-grade support structure exists
Strengths & Limitations
Don Mal built Vena past $100M ARR and co-founded Fluence (sold to Anaplan); CEO Michael Morrison ran Fluence, Jirav and NASDAQ-listed Datawatch. Nobody else in Gen-3 FP&A has a bench that has already built and exited CPM companies — twice.
Agent framework (Supervisor, Planner, Analyst, Modeler), AI Foundation layer and MCP support designed in from day one — not a copilot bolted onto a Gen-2 engine. The most ambitious AI story in the mid-market set.
Pipeline, bookings and churn drive the forecast natively — the capability Nucleus Research specifically praised, and one no direct mid-market competitor centers. For recurring-revenue businesses, this collapses the FP&A/RevOps divide.
A governed cloud model surfaced in Excel — 'keep it or leave it, your choice.' Threads the needle between Cube/Aleph's stay-in-Excel stance and Runway/Abacum's leave-Excel stance, executed by the team that made Excel-native planning a category at Vena.
4-week vendor-guided deployments with the Modeler agent converting existing spreadsheets into structured models; customer evidence (4AG Robotics) supports 'live in weeks.'
UpKeep (5 sheets → 1 model, ~1-hour reforecasts), Northspyre (150MB workbook retired, ~20 hrs/month reclaimed), 4AG (unified 3-country model on NetSuite actuals) — specific and quantified, not logo-wall vapor.
US$13M seed, BPM Partners Best New Vendor 2025, Nucleus Hot Company to Watch 2026, executive-grade CEO hire — an unusually loud first 14 months for a category entrant.
Launched December 2024. One public G2 review. Four named case studies. No disclosed ARR or customer count. Nucleus places it in the entry-level 'Core Provider' quadrant. Every architectural advantage is real but lightly evidenced — you are underwriting a trajectory.
Planning-grade multi-entity aggregation only — no statutory consolidation, eliminations engine or close workflow. The team knows this domain cold (they built Fluence) and chose to defer it; 'CPM expansion' is a 2026 roadmap item, not a capability.
The Excel interface — strategically central to the pitch — has no launch coverage, no public customer evidence and no GA date on record. Demo it against Vena/Cube-grade expectations before weighting it in your decision.
Quote-only pricing with zero third-party benchmarks means you negotiate blind. Mitigate with competing quotes from Abacum/Cube/Drivetrain and the early-stage negotiation levers in the Pricing section.
~US$13M raised, small team, finite runway, no SI ecosystem — and a founder track record of building companies that get acquired. Protect yourself contractually: change-of-control terms, export rights, price caps.
'100+ integrations' vs. three verifiably named connectors (NetSuite, Salesforce, HubSpot). Fine for the core ICP stack; verify everything else for YOUR stack in writing.
No public evidence of large-user-count or massive-dimensionality deployments. Mid-market is the honest ceiling today; Pigment and Anaplan own the proven high end.
Fit Analysis: When to Choose Una
- Mid-market or PE-backed operator whose planning pain is fragmented spreadsheets — multiple disconnected models or one fragile monolith
- Recurring-revenue business where the revenue plan drives everything and pipeline/churn signals should update the forecast automatically
- Lean FP&A team that wants AI agents doing modeling and analysis legwork, and is willing to pressure-test those claims in a demo
- Organization adding entities, products or regions fast enough that model restructuring flexibility matters more than vendor maturity
- Buyer who can extract early-customer economics: design-partner pricing, reference trades, roadmap influence and contractual protections
Cube or Aleph syncs data into the model you already trust, at lower switching cost. Una's rebuild-the-model approach pays off only when the model itself is the problem.
Abacum and Drivetrain deliver strong AI-assisted planning with hundreds of validating reviews. Una's architecture is more ambitious; theirs is more proven. Decide which risk you prefer.
UnaXL promises the Vena experience on a modern engine — but Vena itself offers it with a decade of maturity and a massive template ecosystem. If UnaXL demos short of that bar, Vena remains the safer Excel-native choice.
Recommendation: OneStream, CCH Tagetik, or Anaplan+Fluence. Una consolidates for planning only — the close is out of scope by design.
Recommendation: Pigment for modern enterprise, Anaplan for maximum modeling power. Una's honest ceiling today is the mid-market.
Recommendation: Vena (Excel-native, mature), Planful or Workday Adaptive (established mid-market suites). Revisit Una once the reference base and UnaXL evidence catch up to the architecture.
Demo Questions for Una Evaluation
Una's demo will be impressive — AI-native platforms demo beautifully. These questions are designed to separate architecture from evidence: run the agents on your real data, pressure-test UnaXL against Vena-grade expectations, and get the vendor-viability answers a seed-stage purchase demands.
Frequently Asked Questions
Evaluating Una?
Use the demo questions above to test the AI claims on your own data, and the fit analysis to weigh Una's architecture against its early-stage risk. Start with the vendor-viability questions — they set the negotiating table.