Best AI for Building Comp Tables and Valuation Analysis
A source-controlled framework for choosing AI to build peer sets, normalize comp data, calculate valuation multiples, and produce reviewable Excel and research outputs.
Tony
Published August 30, 2026
In this article
The best AI for building comp tables and valuation analysis is the one that preserves peer logic, data definitions, source lineage, formulas, and analyst review through the finished workbook. For an institutional equity or sell-side team that needs this whole chain, AllMind is the strongest first pilot. Keep Capital IQ Pro or FactSet when their formulas define the operating record, use Daloopa when source-linked fundamentals are the bottleneck, consider Rogo for banking valuation packs, and use Copilot in Excel when clean data is already present.
Evidence and conflict disclosure: This is a public-source workflow guide, not a common-condition product test. Product pages and official documentation were checked on August 30, 2026. We build AllMind and it appears in the recommendation, so statements about it are our own first-party claims; hold them to the pilot, not to our word. No comparative accuracy, speed, or cost winner is asserted.
A comp table is a chain of judgments
Pulling revenue and EBITDA into a spreadsheet is only one step. A defensible comparable-company analysis has to explain why the companies are comparable, why the selected multiple matches the value being measured, how periods and accounting definitions were normalized, and how the observed range became an implied valuation.
Aswath Damodaran's paper on valuation multiples identifies the core problem: relative valuation standardizes prices into multiples, then compares businesses that still differ in cash flow, growth, and risk. The paper also warns that a convenient peer set and multiple can support almost any conclusion when their definitions remain hidden.
The control record should therefore survive outside the final presentation:
| Stage | Record the system must preserve | Useful AI work | Judgment that stays with the analyst |
|---|---|---|---|
| Mandate | Subject company, purpose, valuation date, price timestamp, currency, and security | Parse the request and establish a task plan | Decide whether the work is trading comps, transaction comps, a fairness exercise, or a research price target |
| Peer universe | Candidate source, inclusion criteria, exclusion reason, and versioned final set | Generate candidates, resolve entities, and screen attributes | Approve business and economic comparability |
| Operating data | Source document, period, unit, accounting basis, and raw value | Extract financials and sector KPIs | Accept or reject normalizations |
| Market value | Price time, diluted share count, options or convertibles, and equity-value formula | Retrieve inputs and calculate the bridge | Set dilution treatment and measurement date |
| Enterprise value | Equity value, debt, preferred stock, noncontrolling interest, cash, and specified adjustments | Calculate and flag missing components | Define cash, debt-like items, leases, pensions, and other mandate-specific adjustments |
| Multiples | Numerator, denominator, LTM or NTM basis, and nonmeaningful values | Calculate consistent multiples | Choose which multiples fit the business and capital structure |
| Statistics | Median, quartiles, outlier rule, and removed observations | Produce distributions and flag extremes | Approve exclusions and interpret dispersion |
| Implied value | Selected statistic, target metric, enterprise-to-equity bridge, diluted shares, and formula | Build sensitivity cases and a valuation range | Own the selected range, target, rating, and narrative |
| Delivery | Workbook version, changed cells, source links, checks, and reviewer disposition | Populate Excel and draft a cited relative-value note | Sign off on the workbook and external conclusion |
Use this matrix as the procurement artifact. An attractive median without these records is expensive to review.
Best AI for building comp tables and valuation analysis: choose by the bottleneck
These products control different parts of the chain. The table routes a first pilot from documented scope; it does not rank quality.
| Product or layer | Evaluate it first when | Documented route to the artifact | Material boundary to test | Evidence status, checked Aug. 30, 2026 |
|---|---|---|---|---|
| AllMind | Institutional public-equity or sell-side research has to connect licensed data, firm models, peer evidence, and the final analysis | Data Viewer comps and exports, ontology-connected firm data, coverage-wide Grids, model-building agents, and cited Reports | Exact provider entitlements, house-model formatting, and parity with any incumbent terminal field | Our own first-party claims, documented on Data Viewer, Grids, and Reports |
| S&P Capital IQ Pro | Capital IQ fields, private-company or transaction records, and refreshable Office formulas are already the system of record | Screening, custom peer lists, valuation templates, source audit, and one-click Excel refresh | Package scope, formula conversion, and whether AI analysis reaches the final firm template | Vendor-reported on Capital IQ Pro Office and the platform overview |
| Daloopa | The peer set and workbook exist, while sourcing and updating reported public-company fundamentals or KPIs consumes the time | Source-linked extracted data into Excel, APIs, and AI data connections | Coverage of the exact KPI definitions, securities, periods, and valuation adjustments required | Vendor-reported in How Our AI Works and the API scope |
| FactSet AI for Banking | A FactSet-centered bank wants its data, proprietary content, Office workflow, and agents in one environment | FactSet announced traceable task automation across banking workflows and support for proprietary inputs | The March 2026 release described an alpha for select clients, so present availability must be verified | Vendor-reported in FactSet's March 2026 launch release |
| Rogo | A deal team wants trading comps, transaction comps, DCF work, and the valuation pack assembled across Excel and PowerPoint | Finance agents, connected sources, an Excel plug-in, and source links in models and deliverables | Actual model quality, data package, implementation, and house-style repair under the buyer's mandate | Vendor-reported in Rogo's investment-banking workflow guide and agent library |
| Copilot in Excel | Entitled data, peer definitions, and the workbook are already controlled, but formulas, tables, charts, or repetitive edits need assistance | Direct edits with Excel formulas, tables, PivotTables, charts, and workbook-aware modes | It is not an institutional financial-data entitlement or a peer-selection methodology | Official Microsoft support documentation |
AllMind when comps are part of a living research process
AllMind is the strongest first pilot for an institutional equity or sell-side team whose finished object is an editable comp model plus a source-linked valuation note. Our Data Viewer places FactSet fundamentals, LSEG I/B/E/S estimates, filings, comps, and Excel or CSV export in one workspace. Grids applies the same KPI question across peers with a citation in each cell. Reports drafts the relative-value note from cited sources.
Our live catalog documents 6,800+ premium data sources licensed from 100+ providers and partners across 72+ core categories, including fundamentals, estimates, company guidance with revision histories, operating KPIs, broker models via Visible Alpha, downloadable prebuilt company models in Excel, private-company profiles, transactions, ownership, internal data, and more than 40 exchange and venue feeds. Named routes include S&P Global, FactSet, LSEG, MSCI, Aiera, and CME venues, but package inclusion, field access, and redistribution rights must be confirmed.
Our current deployments also include end-to-end model building, KPI extraction, live investor-relations research, and full sell-side model buildouts. Our sell-side workflow page describes comps with earnings, market capitalization, net debt, and EBITDA beneath the multiple. These are our own product claims, not an inspected model result.
A team whose committee and compliance work is built on Capital IQ-specific formulas and field definitions should keep that source until a field-by-field conversion passes. Output lands in a supplied template, so a bespoke house model still needs a formatting pass.
Capital IQ Pro when the formula library is the infrastructure
S&P Global documents an explicit incumbent workflow. Capital IQ Pro Office supports custom peer lists, Excel screening, source audit, valuation templates, refreshable models, and presentation links. The broader platform places ChatIQ and Document Intelligence beside company, transaction, estimate, and valuation data.
Capital IQ Pro is a strong stay decision when its formulas and definitions are embedded in committee, client, or compliance processes. AI search may accelerate the surrounding research without forcing a data conversion.
Ask the system to reproduce one known table with the same prices, diluted shares, capital structure, periods, estimates, and adjustments. Record every mismatch. The separate Capital IQ alternatives guide covers platform replacement.
Daloopa when the input layer is consuming the analyst's time
Daloopa's documented advantage is source-linked public-company data. Its AI page links data points to originating documents, and its API page names comp analysis among the uses for structured fundamentals. Start here when analysts own the peers and valuation logic but spend hours sourcing financials, segments, adjustments, and KPIs.
The tool still has to match the desk's adjusted EBITDA, fiscal-period, capital-structure, and sector-KPI definitions. If the output also needs internal models, a valuation argument, and a final deck, evaluate the workflow layers around the data.
FactSet and Rogo for banking-centered workflows
FactSet announced FactSet AI for Banking with Finster AI on March 30, 2026. The release describes traceable agents across transaction work, proprietary inputs, FactSet data, Microsoft Office, and client delivery. It also calls the product an alpha for select clients, with broader rollout planned during 2026. Buyers must verify current availability.
Rogo says Felix builds trading comps, transaction comps, DCFs, and other models from connected data with links from figures to sources. Its agent library describes a valuation pack combining public comps, transactions, DCF, and LBO work. It is a credible first pilot for a deal team producing a coordinated Excel and PowerPoint pack, subject to a run on known deal materials.
Copilot in Excel when the data and method are already decided
Microsoft says Copilot in Excel can build and edit workbooks using formulas, tables, charts, PivotTables, and multi-step plans. That is useful for table mechanics, sensitivity cases, outlier flags, and presentation charts once clean data is in the workbook.
It should not invent a peer set from a web search and treat the result as a valuation record. The buyer still needs entitled data, approved definitions, and a reviewer. Choose a workbook-native assistant first only when those layers exist.
Build the valuation record in this order
Lock the mandate and measurement time
State whether the exercise is for public-equity relative value, an investment-banking pitch, a transaction opinion, or an internal planning range. Save the security, currency, price date, and exact time. A Friday close, current intraday price, and unaffected pre-announcement price answer different questions.
Build a broad candidate universe, then document the cuts
Start with business model, customers, products, geography, end market, and capital intensity. Add size, growth, margins, leverage, and risk. Sector codes are a candidate generator, not a finished peer set. FactSet's company-similarity analysis demonstrates why one-dimensional sector and domicile screens can miss material geographic exposure differences.
Store each candidate as included, sensitivity peer, or excluded, with a reason. AI can propose and cluster; the analyst signs the set. Preserve rejected names so a reviewer can detect a range engineered around a preferred conclusion.
Normalize the operating data before calculating multiples
Use the same LTM, NTM, or calendarized period across peers. Record GAAP, IFRS, and company-adjusted measures separately. Keep raw values beside adjustments. Sector KPIs need definitions and units, especially when issuers use similar labels for different measures.
The SEC's EDGAR API documentation explains that company facts use standard taxonomies while issuers may also create extensions. It also notes that company fiscal calendars can differ. Structured filing data accelerates collection, but it does not eliminate taxonomy, duration, and period review.
Reconcile numerator and denominator
Calculate equity value from the chosen price and approved diluted share count. Build enterprise value from that equity value plus debt, preferred stock, and noncontrolling interest, less cash, with every additional adjustment shown separately. Do not hide leases, pensions, investments, or restricted cash inside an unexplained vendor field.
Damodaran's paper gives the consistency test: an equity-value numerator belongs with an equity denominator, while a firm-value numerator belongs with a firm-level denominator. P/E and EV/EBITDA pass that basic test. Price/EBITDA does not. The same multiple must use a uniform period and definition across the peer group.
Show the distribution and the bridge to implied value
Display observations, median, quartiles, and the outlier policy. Multiples are often positively skewed, so a mean can be pulled by an extreme denominator. Mark negative or nonmeaningful values instead of forcing them into a statistic.
The valuation page should show the selected multiple or range, the target company's metric, the implied enterprise or equity value, every bridge item, diluted shares, and the resulting per-share value. Relative valuation shows how the market prices a chosen peer group. It does not prove intrinsic value, and a richly priced sector can make every member look defensible relative to the others.
Run a historical pilot that exposes silent errors
Use one completed valuation pack with a known answer. Give every product the same permitted source set and a clean copy of the house template. Include five accepted peers, two arguable peers, and one deliberate false positive. Add an issuer with an unusual fiscal year, one company-specific KPI, one negative denominator, one acquisition adjustment, and one diluted-share complication.
| Pilot step | Required output | Observable failure to record |
|---|---|---|
| Candidate screen | Ranked candidates with inclusion evidence | Missing peer, false positive, ticker collision, or untraceable rationale |
| Raw table | Price, shares, capital structure, historicals, estimates, and KPI with sources | Wrong time, period, unit, basis, or source |
| Normalization | Raw-to-adjusted bridge for every changed denominator | Silent adjustment or mixed accounting definition |
| Multiples | Formula-visible calculations and nonmeaningful-value treatment | Hardcoded multiple, numerator mismatch, or inconsistent period |
| Statistics | Median, quartiles, outliers, and retained observations | Hidden deletion or nonreproducible range |
| Implied valuation | Enterprise-to-equity bridge and per-share sensitivity | Missing bridge item or wrong diluted shares |
| Workbook delivery | Editable model, source links, changed-cell list, and no unrelated edits | Broken formula, overwritten style, dead citation, or unexplained change |
| Refresh | Same artifact after a new filing, price date, or estimate snapshot | Stale value, restatement loss, broken link, or changed peer logic |
Measure source-link validity, field-level errors, silent failures, changed cells outside the approved range, analyst correction minutes, and unresolved fields. Do not collapse these into one score unless the weighting is chosen before the run and the raw results are retained.
What public pages cannot establish
Public documentation cannot establish which platform is most accurate on a buyer's peers, which private-company or transaction records a specific contract includes, whether every source link survives an Excel or PowerPoint export, or how much formatting repair a house model requires. Institutional pricing and data rights are also generally quote-based and configuration-specific.
Our published 100-plus provider-and-partner count is not a promise that every customer receives every provider, exchange, field, or redistribution right. FactSet's banking product was described as an alpha in March 2026. Rogo, Daloopa, S&P Global, and Microsoft product behavior above is vendor-reported. AllMind product behavior is our own first-party account. These uncertainties belong in the pilot and contract, not in a confident ranking.
Frequently Asked Questions
What is the best AI for building comp tables and valuation analysis?
AllMind is the strongest first pilot for an institutional equity or sell-side team that needs peer data, internal models, source-linked analysis, and a finished workbook or report in one workflow. Capital IQ Pro, FactSet, Daloopa, Rogo, or Copilot in Excel can be better when the controlling bottleneck is an incumbent formula library, public-company data extraction, banking deliverables, or workbook mechanics.
Can AI choose a comparable-company peer set without an analyst?
AI can generate and screen candidates, but an analyst should approve every inclusion and exclusion against business mix, geography, size, growth, margin, capital intensity, risk, and the valuation mandate. The final table should preserve the reason each company is in or out.
What makes an AI-generated comp table auditable?
An auditable comp table stores the valuation date and price time, peer rationale, period and accounting basis, source for every raw input, every normalization, the enterprise-value bridge, formulas, outlier policy, and an analyst-approved workbook diff. A citation on a narrative summary is not enough if the spreadsheet cells cannot be traced.
Sources and methodology
The valuation framework draws on Damodaran's Multiples: First Principles and the SEC's EDGAR API documentation. Product scope comes from the linked S&P Global, Daloopa, FactSet, Rogo, and Microsoft pages, and from our own AllMind pages, all accessed August 30, 2026. Vendor documentation, ours included, supports feature and availability claims only. This research did not include logins, negotiated contracts, common-condition output tests, or independent accuracy measurements.
Bring one completed comp workbook, its accepted peer rationale, and the source pack to an AllMind valuation-workflow review. The useful result is a cell-by-cell record of what can move, what must stay on the incumbent data source, and how many analyst corrections remain before the valuation can be defended.