Deliverables AI Alternatives: Memos, Models and M&A Analysis

Executive Summary

Corporate development, strategy and M&A teams are being sold "AI for deals" by at least five different kinds of vendor at once, and the products are not substitutes for one another. Deliverables AI is a narrowly focused deliverable factory: upload source material, ask in plain English, and receive a cited CIM, teaser, IC memo, screening memo, board deck or Excel model, priced at $25–$250 per seat per month on a credit meter [1][8]. CorpDev.Ai is a broader corp-dev operating system that bundles an agentic analyst, an AI-native data room, a document workbook, a zero-entry pipeline CRM, target sourcing across 70M+ companies and market mapping, priced at $1,000–$3,000 per month for one to three seats [13][15]. Between and around them sit the venture-backed finance copilots Rogo (>$165M raised, 25,000+ daily users) and Hebbia (~$161M raised), general-purpose enterprise AI (Microsoft 365 Copilot at $30/user/month, ChatGPT and Claude Enterprise), and the incumbent systems of record — Midaxo, DealCloud, Affinity, Datasite, Ansarada [18][17][71].

The verdict in three sentences. Buy Deliverables AI if your bottleneck is producing polished documents and models from material you already have, you work in Word, PowerPoint and Excel, and you want the lowest-risk entry price in the category — but recognise it is a very young company (founded 2024, an estimated ~$220K ARR, no disclosed funding) whose product deliberately stops at the edge of the data room and the CRM [6][10]. Buy CorpDev.Ai if the problem you are solving is the whole upstream workflow — sourcing, screening, monitoring, pipeline and diligence over large data rooms — and you are prepared to pay roughly eight times more for one integrated system and to accept a vendor of comparable early-stage maturity. If your firm already has Microsoft 365, include Copilot in the evaluation: at $30 per user it is the floor every specialist tool has to beat, and the honest comparison is "specialist tool plus Copilot" against "Copilot alone" [71].

The categories are production/analysis, ongoing CRM/process and transaction execution. Deliverables AI concentrates on production; CorpDev.Ai spans analysis and parts of the operating workflow. Scope, licence minimums and data entitlements prevent a simple price ranking.

Five things a buyer should take away from this comparison:

• Scope, not model quality, is the real differentiator. All of these vendors route to the same handful of frontier models (CorpDev.Ai states it orchestrates Anthropic, OpenAI, Perplexity and Google; the others are similar) [13]. What you are paying for is the workflow, the data connectors, the citation and audit layer, and the output formats — so evaluate those, not the demo prose.
• Deliverables AI's credit meter is generous for a small team but must be modelled. A Pro seat carries 20,000 credits per month; a long-form CIM typically costs ~6,500 and can reach 19,000, a financial model ~3,000 [8]. A two-seat team doing three CIMs and a handful of models a month sits inside its allowance; a team that iterates heavily will buy top-ups at $0.01–$0.02 per credit.
• CorpDev.Ai's price buys breadth you may or may not need. The AI Pro Team plan includes CRM, sourcing, monitoring and market mapping — functions a corp-dev team otherwise licenses from Affinity, Grata or Midaxo at $6,000–$120,000 a year [21][25][19]. If you need several of those functions, compare the actual module and seat quotes: the cited ranges do not establish that any two-tool combination necessarily costs more than $36,000; if you only need documents, it is over-buying.
• Neither of the two focal vendors is a "safe" enterprise procurement. Both are small, founder-led and lightly disclosed. Rogo, Hebbia, Midaxo and DealCloud carry materially lower vendor-viability risk, at materially higher prices [18][17]. Contract for data export and exit rights accordingly (Section 6).
• Test on your own data room before you sign anything. Public accuracy claims in this category are not comparable across vendors; a 50–100 question side-by-side pilot on an anonymised deal is the only evidence that transfers [92].

1. What Problem Are You Actually Buying For?

Most bad software decisions in corporate development come from comparing tools that do different jobs. A corp-dev or strategy team runs four distinct workflows, and the AI vendors now pitching to that team each anchor on one of them and stretch toward the others.

Deliverables AI is a job-3 product with a serious job-2 capability and a token job-4 feature. Its own documentation is candid about this: it "does not directly connect to VDRs or data rooms by design," expecting the team to download from Datasite or Intralinks and upload [6]; its Deal Tracker is "a lightweight deal-process coordination layer, not necessarily a full CRM" [7]. CorpDev.Ai is a job-1-and-4 product that has built job-2 and job-3 capabilities on top — its pitch is explicitly "strategy → sourcing → due diligence → execution → integration" [14]. Rogo and Hebbia are job-2 products that have expanded into job 3 [16][18]. Copilot, ChatGPT and Claude are job-2/3 generalists with no notion of a deal at all.

The original simplified matrix marked CorpDev.Ai in all four stages; breadth does not establish equal specialist depth. Compare the licensed core product with current suite extensions, and measure review effort and output quality.

The practical consequence is that the question "Deliverables AI or CorpDev.Ai?" is usually the wrong first question. The right sequence is:

1. Which of the four jobs is your binding constraint today? A two-person corp-dev team at a mid-cap that closes one deal a year is constrained by production time (job 3) and can live with a spreadsheet pipeline. A programmatic acquirer screening 300 targets a year is constrained by sourcing and process (jobs 1 and 4).
2. What do you already pay for? If Affinity or Midaxo is in place and working, CorpDev.Ai's CRM is redundant and its price premium over Deliverables AI is harder to justify. If nothing is in place, the bundle argument favours CorpDev.Ai.
3. What is your Microsoft or Google position? Copilot at $30 per user per month and Gemini at $27–$35 already cover a surprising share of job 3 for teams whose material sits in SharePoint or Drive [71][77]. Any specialist tool has to show incremental value over that floor.
4. How much vendor risk can procurement tolerate? Both focal vendors are early-stage. If your IT security team requires a SOC 2 Type II report, multi-year viability evidence and a negotiated DPA, Rogo, Hebbia, Midaxo or DealCloud will clear that bar more easily — at three to thirty times the price.

2. Deliverables AI in Depth

What it is

Deliverables AI describes itself as an "AI deal assistant" for investment banking, M&A advisory, private equity, corporate development and strategy teams. The user uploads source files — CIMs, management presentations, financial statements, diligence reports, market research, data-room downloads — and asks for a deliverable in plain English. The platform synthesises the material and returns an editable, versioned Word, PowerPoint or Excel output with citations back to the sources [1][2]. The product is organised as a library of "skills": CIM and teaser generation, buyer and target lists, deal screening, IC memo, due-diligence checklist, DCF and merger models, market research, board decks and a Deal Tracker [2][3][4][7].

Skills relevant to a corp-dev buyer

Pricing, and how to read the credit meter

Pricing is per seat with a shared pool of credits that meter actual compute consumed [8]:

A 30-day free trial with 10,000 credits and no credit card is offered. Top-up credits cost $0.01 each bought ahead or $0.02 on auto-reload and never expire; included plan credits reset monthly [8].

The published typical costs per task let a buyer size a seat properly [8]:

Worked example for a corp-dev pair on Pro (40,000 pooled credits a month, $250): four screening memos (~7,600), one IC memo (~1,900), one DCF (~3,000), one board deck (~6,500) and a hundred chat lookups (~10,000) consume roughly 29,000 credits — comfortably inside the pool. A team that regenerates a long CIM five times in a month to iterate on narrative could exhaust the same pool on that task alone. The meter therefore rewards disciplined prompting and penalises trial-and-error; a buyer should run the trial with real material and watch the balance before choosing between Pro and Max.

Customers and evidence of value

Public evidence comes almost entirely from company-published case studies at boutique advisory firms rather than from corporate development teams [12]:

• Rowe | Tomes Advisors (sell-side): a CIM, teaser, buyer list and diligence checklist process described as taking six weeks reduced to roughly ten days; the firm reports running more mandates in parallel without adding headcount [5][12].
• TechStrat: data-room review roughly one-third faster; competitive-pitch win rate reported to have risen from 34% to 42% [12].
• Solganick: qualified founder conversations up about 25% (12 to 15 per month) from origination research [12].

No independent G2, Capterra or analyst coverage of any depth was identified, and no corporate-development reference customer is published [2]. The company is a 2024 start-up; GetLatka estimates 2025 ARR at about $220,000 and records no disclosed funding — a third-party estimate, not a company figure [10].

Strengths

• Lowest entry price in the category, with transparent, self-serve pricing. A two-person team can start for $250 a month with no procurement cycle; nothing else in this comparison, apart from general-purpose copilots and Eilla, comes close [8].
• Finance-native output formats. Editable DOCX, PPTX and XLSX with version history, firm templates and cited sources — the formats an IC or board actually consumes [1][8].
• Skills map to real corp-dev tasks. Screening memos, IC memos, DCF and merger models are exactly the recurring artefacts a small team struggles to produce fast.
• Honest scoping. The vendor states plainly what it does not do (VDR connection, full CRM), which is unusual in this category and makes the product easier to slot into an existing stack [6][7].
• Stated enterprise controls: server-side workspace isolation, encryption, no training on customer data [2].

Limitations and risks

• No data-room connector and no persistent knowledge base across deals. Every engagement starts from uploads; there is no continuous company intelligence, monitoring or trigger feed [6].
• Thin sourcing. The target-list skill draws on B2B contact data but is not a substitute for a Grata- or CorpDev.Ai-class sourcing layer with semantic search across tens of millions of companies [2].
• Pipeline is a tracker, not a CRM. No email or calendar sync, no relationship intelligence, no activity capture [7].
• Credit metering creates variable cost and a behavioural incentive to under-iterate; heavy users should budget top-ups or move to Max.
• Customer evidence is sell-side and self-published. The corp-dev use case is asserted on the solutions page but not yet demonstrated by a named reference [2][12].
• Model outputs need finance review. As with every tool in this category, DCF and merger models are first drafts; assumption logic, accounting treatment and transaction mechanics must be checked by a professional [2].

Bottom line on Deliverables AI: it is the most cost-efficient way in 2026 to convert deal material into finished documents and models, and the cleanest "add-on" to an existing corp-dev stack. It is not a system of record, not a sourcing engine and not a data room, and its corporate-development credentials are still to be proven by reference customers.

3. The Alternatives

3.1 CorpDev.Ai — the end-to-end corp-dev operating system

What it is. CorpDev.Ai positions itself as "the most comprehensive end-to-end M&A platform" — an agentic system spanning strategy, sourcing, diligence, execution and integration, built by founders who have both built M&A software and run deals. CEO Kal Kilpi co-founded Midaxo and founded Vastuu Group (acquired); co-founder Atul Tiwary was VP M&A at Barracuda Networks under Thoma Bravo, VP Investment Banking at RBC and Senior Director of Corporate Development at Fortinet [13][14]. That pedigree matters: the product is designed around the workflow of a repeat corporate acquirer rather than a banker's pitch cycle.

Modules. The published feature list covers seven areas, all included from the entry plan [13][15]:

Pricing. Verified from the vendor's pricing page on 11 September 2026 [13]:

Two pricing facts a buyer should weigh. First, financial modelling is an Enterprise-exclusive capability — a strategy team that wants AI-built DCF or merger models on the self-serve plans will not get them here, whereas Deliverables AI includes them at $25 [13][8]. Second, the plans meter search credits (1,000 per seat per month), not generation; the pricing page does not publish a per-task consumption table equivalent to Deliverables AI's, so a buyer should ask what a heavy sourcing month consumes.

Evidence. The company cites "hundreds of CorpDev professionals" and states SOC 2 Type II compliance, encryption at rest and in transit, and no training on customer data [13]. No named reference customers, funding rounds or revenue figures are published; like Deliverables AI, it should be treated as an early-stage vendor for procurement purposes.

Strengths for a corp-dev buyer

• One system for the upstream workflow. Sourcing, monitoring, pipeline and diligence in one place with a shared knowledge layer ("CorpDev Brain") that learns across deals — the thing Deliverables AI structurally lacks [13].
• Zero-entry CRM. Automatic capture from email and calendar removes the data-entry burden that kills most corp-dev CRM deployments.
• Data-room scale. The AI Room is designed for whole-room ingestion with page-level citations, competing with Hebbia and Keye rather than with document generators [13].
• Bundle economics. At $36,000 for three seats it undercuts the combination of Affinity ($6,000–$8,100), Grata ($15,000–$45,000 per seat) and a Hebbia-class analysis tool (~$30,000) [25][19][17].
• Exports to standard formats and an explicitly "open, traceable" architecture position, which reduces lock-in relative to closed platforms [14].

Limitations and risks

• Price-to-value depends on using the bundle. A team that only needs documents pays roughly eight times Deliverables AI's Pro price for capabilities it will not use.
• No self-serve financial modelling on AI Pro or AI Pro Team [13].
• Marketing claims are aggressive ("100× faster, at 1% the cost") and unverified [13].
• Early-stage vendor with no disclosed funding or named references; the same viability caveats as Deliverables AI apply.
• Not a VDR. Like Deliverables AI, it analyses files but does not replace Datasite or Ansarada for a live sell-side process.

Bottom line on CorpDev.Ai: the strongest option in this comparison for a team whose bottleneck is sourcing, monitoring and pipeline discipline rather than document production, and the only one that credibly consolidates three or four line items into one. It is over-specified — and over-priced — for a team that simply wants better IC memos.

3.2 AI deliverable and analysis peers — Rogo, Hebbia, Eilla and others

These are the vendors a corp-dev buyer will most often see in the same RFP as Deliverables AI: products whose primary job is analysing deal documents and producing finance-grade output.

Rogo is the best-funded and fastest-growing product in the category. It announced a $75M Series C led by Sequoia in January 2026, bringing total funding to more than $165M, opened a London office and reported over 25,000 daily users at firms including Rothschild & Co, Jefferies and Lazard [18]. In September 2026 it announced a partnership with Datasite and the acquisition of Arvo, an AI meeting assistant for financial institutions, and disclosed strategic investment from large financial institutions [18]. Third-party trackers report a subsequent round at a materially higher valuation and an ARR run-rate in the tens of millions; those figures are estimates and are not confirmed by the company [81][83]. For a corp-dev buyer Rogo's appeal is a finance-trained copilot that lives inside the Office applications the team already uses; its constraints are enterprise-only sales, pricing that is not published and a product tuned to banks and funds rather than corporate acquirers. It is not a CRM, a sourcing engine or a data room.

Hebbia built its reputation on "Matrix," a grid-based interface for asking hundreds of questions across thousands of documents with page-level citations. Matrix 2.0 (January 2026) extends the product from analysis to final deliverables — memos, decks and models [16]. It raised $130M at a ~$700M valuation in 2024 on about $13M of revenue [87], publishes SOC 2 Type II and TLS 1.2+ [96], and its DPA permits processing in any country where it or its subprocessors operate — a point for EU buyers to negotiate [98]. Market estimates put a professional seat at ~$10,000 per year and ARR around $48M in mid-2026 [17]. Hebbia is the benchmark for data-room-scale analysis; a corp-dev team should test CorpDev.Ai's AI Room and Deliverables AI's data-room skill against it, not the other way round.

Eilla AI is the closest price peer to Deliverables AI. A Bulgarian-founded company with $1.5M of seed funding, it offers research, investment analysis and IC-memo workflows with a free tier and a $329-per-month five-seat Professional plan [30][90]. It has pivoted partly toward AI-native M&A advisory for SMBs, executing what it calls Europe's first AI-native M&A deal in April 2026 [28]. It is a credible low-cost option for a boutique; corp-dev buyers should treat it as an experiment rather than a platform.

Others worth knowing, briefly. Mosaic ($18M Series A, April 2026) is the most capable automated LBO/transaction-model builder and a natural add-on for PE-style analysis [55]. Keye ($5M seed; 20+ funds, $1.4T AUM) is a PE diligence analyst rather than a document producer [39][40]. Blueflame was acquired by Datasite in 2025 and now sits inside the VDR vendor's enterprise offer [33]. Bridgetown Research ($19M Series A) sells AI research agents that behave like an outsourced commercial-diligence team [50]. Finster ($15M raised) is financial intelligence and deal monitoring for banks and asset managers [61]. Macabacus ($200–$360 per licence per year) is not generative AI at all but remains the quiet productivity layer many banks pair with any of the above [57].

3.3 General-purpose AI — ChatGPT Enterprise, Claude, Microsoft 365 Copilot

Every specialist vendor in this comparison is, under the hood, orchestrating one or more of the same frontier models a buyer can license directly. The generalists therefore set the price floor and the honest baseline for any pilot.

What the generalists do well. For a corp-dev team already on Microsoft 365, Copilot summarises a CIM, drafts a first-pass screening note, explains an unfamiliar model and rewrites a slide — inside the files, under existing permissions, at $360 per user per year [71]. Claude and ChatGPT are stronger at long-document reasoning and increasingly capable of building and auditing Excel models [67][64]. For everyday drafting, this is where the marginal dollar goes first.

Where they fall short. Four structural gaps explain why the specialist category exists [65][70][76]:

• No native deal system of record. General-purpose tools can retain project context or be configured around deal concepts, but the buyer must supply and maintain the target, pipeline, buyer-list and diligence structure. A generic chat alone does not establish an auditable deal record.
• Corpus limits. ChatGPT Enterprise projects cap at 40 files; Claude's usable context is bounded by usage capacity; Copilot retrieves rather than ingests. None can be pointed at a 5,000-file data room and trusted to have read all of it.
• Citations are navigational, not evidential. All four can cite, but none guarantees exhaustive retrieval or correct page references; a reviewer must open the source.
• Finance workflows require configuration and review. Producing a bank-quality CIM or linked three-statement model requires suitable templates, instructions and financial review. Specialists package these workflows, but their availability does not establish superior output accuracy.

3.4 Adjacent categories a corp-dev buyer will be cross-sold — CRM, VDR, sourcing data, legal review

Neither Deliverables AI nor CorpDev.Ai replaces every incumbent in a corp-dev stack. The table sets out what each adjacent category does, what it costs, and whether the two focal vendors overlap with it.

Reading the table. Deliverables AI is complementary to almost every incumbent: it sits downstream of the VDR and the CRM and turns their contents into documents. That makes it easy to add and easy to remove. CorpDev.Ai is substitutive for three of the six categories — CRM, sourcing data and market intelligence — which is the basis of its bundle pricing and also the reason its implementation is a bigger decision: replacing Affinity or Grata means migrating history and retraining a team.

Deliverables AI primarily adds production over the existing stack. CorpDev.Ai can consolidate more layers, but does not automatically replace every data licence, CRM control or specialist review. Microsoft 365 Copilot may sit alongside either at the cited $30/user/month add-on price.

4. Head-to-Head Comparison

Capability matrix

Ratings reflect published product scope as of September 2026: ● native, core capability; ◐ present but partial or lightweight; ○ absent. They describe what the product is designed to do, not measured quality — quality is what the pilot in Section 6 establishes.

Sources: vendor pricing and product pages [1][2][6][7][8][13][15][16][18][21][25][71].

Price normalised to a three-person corporate development team

Programme-scope assessment. The CorpDev.Ai integration entry describes its end-to-end management scope rather than assigning an unsupported comparative performance score. Evaluate the required controls and the quality of completed work on the same acquisition programme as other finalists. Deal frequency and public review volume do not establish a functional ranking. See the lifecycle framework and integration capabilities; these are vendor materials, not independent benchmarks.

Basis note: published prices are used where they exist; Rogo, Hebbia and DealCloud figures are third-party market estimates with an upper bound added for typical enterprise minimums, and should be confirmed by quotation.

Workflow-fit scoring

The table scores each vendor against the four corp-dev jobs from Section 1 on a 0–5 scale, weighting equally. Scores are the author's judgement from published scope and the evidence cited in Sections 2–3, and are intended to structure a shortlist, not replace a pilot.

Two readings follow. On breadth, CorpDev.Ai has the highest total in this rubric; Deliverables AI also scores on all four jobs, with a lighter process capability. On cost per unit of capability, Deliverables AI and Copilot dominate, because they are cheap and each does one thing well. A buyer who values breadth pays roughly five times more per point for it; whether that is worth it depends entirely on whether jobs 1 and 4 are real constraints — which is the question Section 5 puts numbers on.

5. Total Cost of Ownership: Three Team Scenarios

List price is the smaller part of the decision. The scenarios below add the tools each team realistically needs alongside the focal vendor, using the published or estimated prices from Section 4, and state the assumptions so a reader can substitute their own.

Scenario A — the lean pair: two-person corp-dev function at a mid-cap, one to two deals a year

The constraint is production time. Sourcing is opportunistic and banker-led; the pipeline fits in a spreadsheet; the data room is whatever the seller provides.

Verdict: Deliverables AI. The 3.4× premium for CorpDev.Ai buys capability this team will not exercise, and the absence of self-serve financial modelling on the AI Pro plan is a real gap for a strategy-led acquirer.

Scenario B — the programmatic team: three people, five to ten deals a year, 200+ targets screened, no CRM in place

The constraints are sourcing coverage, pipeline discipline and keeping intelligence current across many live conversations.

Verdict: CorpDev.Ai, narrowly and conditionally. Its bundle lands in the middle of the layered range while eliminating two integrations and giving the team one knowledge base. The condition is that its sourcing and CRM modules perform at least as well as Grata and Affinity on the team's own target universe in a pilot; if they do not, the layered stack at the low end of its range ($27,600) is the better buy.

Scenario C — the large corporate: eight-person corp-dev team, Midaxo or DealCloud already deployed, IT security gate

The constraints are analyst throughput on large data rooms, consistency of IC materials and procurement risk. The system of record is not in play.

Verdict: Rogo or Hebbia for a team whose procurement function will not accept an early-stage vendor; Deliverables AI Max as the low-cost challenger if it passes security review and the pilot. CorpDev.Ai's case here is weaker because its CRM and pipeline are redundant with the incumbent, unless the team is actively looking to replace Midaxo or DealCloud.

Basis note: Scenario C shows no CorpDev.Ai figure because the Enterprise plan is quote-only; the "enterprise copilot route" column shows Copilot/Claude alone in Scenario A and Rogo-to-Hebbia in Scenario C. All Rogo, Hebbia and Grata figures are market estimates.

6. What Buyers Should Test Before Signing

Vendor accuracy claims in this category are not comparable and should not be used to shortlist. FinanceBench testing found GPT-4-class retrieval systems failing or hallucinating on roughly 81% of a 150-question sample of filing-based questions; more recent grounded systems report low-single-digit hallucination rates under controlled conditions — but neither figure is a production rate for any commercial M&A product [92][93]. The only evidence that transfers to your team is a pilot on your own material.

Pilot design

1. Use one closed deal you know the answers to. Anonymise it, include the awkward material — scanned PDFs, a contradictory earlier version of the model, a contract with a buried change-of-control clause — and write 50–100 questions with agreed correct answers before any vendor sees the pack.
2. Run every vendor and a generalist control on the identical pack. Ask each for the same three deliverables: a one-page screening memo, an IC memo and a DCF.
3. Score separately for retrieval precision, recall, numerical accuracy, entity accuracy, citation correctness, abstention quality ("not found" instead of a guess), cross-document consistency and the share of outputs a reviewer had to correct before use [92].
4. Time the whole loop, not the generation: upload, prompt, review, correct, export.
5. Compare cost per reviewer-approved deliverable, not cost per seat.

Security and data questions to put in writing

• Current SOC 2 Type II report (scope, period, exceptions) and/or ISO 27001 certificate — Hebbia publishes SOC 2 Type II; CorpDev.Ai states SOC 2 Type II on its pricing page; Rogo's trust centre lists ISO 27001 and 42001; Deliverables AI states isolation and encryption but a buyer should request the report [96][13][97][2].
• No-training commitment in the contract, not only in marketing. All four generalists and all focal vendors state it; confirm that optional feedback programmes are disabled.
• Data residency for production data, backups, logs and embeddings, and where inference runs. Hebbia's DPA permits processing wherever it or its subprocessors operate — EU buyers should negotiate [98].
• Subprocessor list — which frontier-model providers receive your data, under what terms.
• Retention and deletion on termination, including backups, with certification.

Lock-in and exit checks

The practical rule: if you cannot reproduce the work product from what the vendor exports, you are locked in [99][100]. Require an exit schedule covering original files, extracted text, prompts and answers, citations, generated deliverables, audit logs and workflow or skill configuration in machine-readable formats — with a defined export window, no punitive fees and deletion certification. Deliverables AI's DOCX/PPTX/XLSX-native outputs and CorpDev.Ai's Markdown-native workbook with standard-format exports are both structurally favourable here; the risk with either is the accumulated context (memory, knowledge layer, CRM history), which is what to test exporting.

Commercial protections for an early-stage vendor

7. Recommendation by Buyer Profile

The decision in one picture

Final assessment

Deliverables AI is the best-value product in this comparison for the job it chooses to do — turning deal material into finished, cited, editable documents and models — and its self-serve pricing removes the procurement friction that keeps most corp-dev teams from adopting AI at all. Its weaknesses are the ones its own documentation admits: no data-room connection, no CRM, no persistent intelligence across deals, and a company still early enough that a buyer should contract defensively.

CorpDev.Ai is the most complete answer to the corporate-development workflow as a whole, with potential to consolidate several existing subscriptions, subject to equivalent capability and entitlements. Its price reflects that breadth, its self-serve plans lack financial modelling, and its claims are — like most in this category — ahead of its published evidence. For the programmatic acquirer building a stack from scratch it is the strongest candidate; for everyone else it is a platform decision that deserves a pilot, not an impulse purchase.

Rogo and Hebbia are where a buyer goes when vendor maturity, bank-grade references and depth on very large corpora outweigh cost. Microsoft 365 Copilot is not an alternative so much as the baseline every one of these tools must beat, and at the cited $30 a seat it is a useful baseline where the existing Microsoft environment and pilot results support it.

The professional's move in 2026 is therefore not to pick a winner from a feature list but to run a four-week pilot with a known deal, a generalist control and a written rubric — and to let cost per reviewer-approved deliverable, not cost per seat, decide.

Key Facts & Sources

Load-bearing figures used in this comparison, with source and as-of date. "Published" means read directly from the vendor's page on 11 September 2026; "estimate" means a third-party market figure that should be confirmed by quotation.

Derived figures. Three-seat annual costs in Sections 4 and 5 are computed directly from rows 1, 7, 11, 13, 14 and 16 (e.g. Deliverables AI Pro: 3 × $125 × 12 = $4,500; CorpDev.Ai Team: $3,000 × 12 = $36,000; Copilot: 3 × $30 × 12 = $1,080). Workflow-fit scores in Section 4 are the author's judgement from published scope and are labelled as such. Cost per weighted point = annual cost ÷ weighted score.

Known gaps. No independent review-platform data exists for Deliverables AI or CorpDev.Ai; neither publishes funding, audited revenue or named corporate-development references. Rogo's post-January-2026 financing has been reported by trackers but not confirmed by the company in the material reviewed. Hebbia's current seat price and customer count are estimates.