dealgraph by dealmkr
dealmkr connects your sales conversations, CRM activity, external signals and company knowledge into a shared foundation of facts and evidence. Put it to work with dealmkr's sales agents, or connect your own AI through MCP.
Connect in an afternoon, see impact in days.
Illustrative, one deal over four months: how the dealgraph is built for Northwind Retail, a new business deal, shown once every step has run. First, connect your tools and data sources. Six sources are connected: declared by you, 21 documents; CRM, 18 records; email, 65 messages; calendar, 7 meetings; calls, 7 recordings; and external data, 4 providers. What they hold passes through three steps. Extract: every fact is pulled with its source, speaker and time intact, such as Priya Nair, 00:14:02 into a Gong call on 12 Aug, saying “I’ll take this to Marcus and push for Q1 budget.” Validate: conflicts are resolved on record and the losing candidate is kept with the reason it lost; Priya Nair on the call and P. Nair in the CRM are the same person, and Marcus Bell lost the champion claim, 1 source against 3. Normalize: facts are typed to one schema, append-only and versioned, so the record reads champion, Priya Nair, observed, from calls and email, 14 of 14 inputs read, since 12 Aug. Second, build your dealgraph, where one tile is one recorded fact or open question, across four contexts. Your company, the interpretation layer: products, pricing, playbooks, stages and exit criteria, declared once and versioned, so everything inside is read through it and “champion” means what your playbook says it means. Account: their business, people, systems and news. Deal: needs, stakeholders, commitments and progress. Rep: how this rep sells, and what they do next. Every tile is in one of three states: a recorded fact, filled in its source’s color; an inferred assessment, the same fill with a dot at its center; or a missing detail with no source, left empty. With all six sources connected, coverage is 93%. Third, supercharge your agents: every query runs over the record, and the answers are grounded. The chat agent, asked why Northwind has stalled, answers that champion Priya Nair has gone quiet for 21 days, the economic buyer missed two meetings, and no business case appears in 22 inputs, from 9 facts across calls, email, calendar and CRM; it rests on the deal, rep and account contexts. The action agent’s next best action is to get the business case documented with Priya before the 14 Oct pricing call, from 3 conditions, and it resolves when the fact changes; it rests on the deal and your company. Artifact agents write a mutual work plan that names the objection raised and the number actually discussed and flags budget as missing, from 12 facts with every line traceable; they rest on all four contexts. Your own agents, built by your GTM engineers and connected via MCP, ask for a champion by name and get it back typed, with its evidence, its time and its coverage; they reach the whole record, your models and your tokens.
Thousands of signals per deal across a dozen systems, most already captured. Almost none of it is in a form anything can act on.
Diagram: thousands of signals from one deal over four months, from calls, email, calendar, documents, enrichment and CRM changes, passing through a form into a five-field opportunity record.
The other 72% goes to logging what happened, chasing fields, and pulling context out of five tabs to prepare for one call. Work no buyer ever sees.
What reaches them is a stage, an amount and a date. Fewer than half trust the number built on it, and the signals that would explain a deal never make it into anything they can read.
Calls recorded, threads captured, enrichment subscribed. No rep reads all of it, so execution still depends on what one person happened to remember.
A better model over a partial picture is still partial, and faster. The output looks confident and is quietly wrong, which is worse for a seller than no output at all.
Sources 28%: Salesforce, State of Sales, 2022 survey of 7,775 sales professionals · 45% and 47%: Gartner, State of Sales Operations Survey, 2020 · 84%: Gartner, 2023 survey of 303 sales leaders
To get the benefits of software we compressed reality into a form, and split what survived across a dozen systems that never talked. Deals are arguments between people, and the only place one was ever whole was a rep's memory.
And the obvious fix does not fix it. Plenty of teams have wired an agent into Gong, Drive, Slack and the CRM, and it reads all of it. What breaks is what happens next.
It re-reads the transcript every time. Nothing becomes a durable fact. Ask twice, get two answers.
Diagram: one transcript, asked the same question on Monday and Tuesday, returns Priya Nair once and Marcus Bell once, with nothing stored in between.
Two sources disagree and it picks one, silently, and differently next time. “Priya” and “P. Nair” stay two people.
Diagram: Priya Nair from a Gong call and P. Nair from a Salesforce contact, joined by a dashed line with a question mark, the same person never resolved.
No sense of time, and none of itself. Change the prompt and every past answer changes, with nothing recording that it did.
Diagram: six answers under prompt v1 in blue, the same six under prompt v2 in red, every past answer changed with no record of why or when.
Retrieval is not a record. Reading is not remembering.
Every team has someone who wired an agent to Gong, the CRM and Slack in a week. The demo was great. Then it hit three walls.
Chart, illustrative: quality against effort and time. A prototype reaches demo quality in a week and then climbs slowly toward a dashed production-quality line, where the return is. The same build on bad data plateaus lower and declines. The gap between them is the last mile, paid for in data work.
Getting started is not the hard part, and nearly everyone has. The prototype was never the problem.
Only a third of organizations have begun to scale AI at all. Over half of GenAI projects were abandoned after proof of concept, and the hours the prototypes saved mostly went nowhere.
Poor data quality is Gartner's first-named cause. Only 47% of sales teams believe their own data is high quality, and every AI initiative inherits that number.
The last mile is a data problem, not a model problem.
Sources 88% and one third: McKinsey, State of AI 2025, n=1,993 · 48% and 8 months: Gartner survey, May 2024 · over half abandoned: Gartner, 2026 · 60%: Gartner, Feb 2025, survey of 248 data leaders · 47%: Gartner, 2020
dealmkr's dealgraph supercharges GTM agents with relevant context and comprehensive data, all grounded in evidence-based facts.
Ask anything of the record: what is holding a deal up, which commits are real, what changed since Friday. Answers cite their facts and say what they do not know. Included.
Your playbook, declared once, read every day against every deal. Reps see what to do, why, how, and what resolves it. Fires when a condition is true, resolves when the fact changes, never a to-do list that rots.
Pitch, mutual work plan, proposal and business case, built from what actually happened, naming the objection that was raised and the number really discussed, and saying so where a fact is missing.
Change queries over the record: what moved, what slipped, where the data is thin, which behaviors correlate with winning.
Query any field across the whole book, see what each value rests on, see the candidates it beat and why, then accept, override or flag the producer.
The dealgraph as a tool your agents can call. Ask for a champion by name and get it back typed, with its evidence and its coverage. Your automations, your models, your tokens. Pilot access for GTM engineers.
The record assembles itself, so preparing for a call is reading rather than reconstructing. Time moves from logging what happened to deciding what to do next.
Your stages, your exit criteria, your qualification bar, declared once, then applied to every deal. A stage cannot be skipped quietly, and a commit that fails your own criteria is visible before the quarter closes.
Every insight can be opened: the deal it came from, the statement behind it, who said it and when, and what nobody checked. The argument moves from whether a number is real to what to do about it.
Chat, action and artifact agents work immediately because they run on a complete record. Your GTM engineers get a structured foundation and an open interface, so what they build inherits the same evidence, coverage and provenance.
And it compounds. Every source connected, every correction made and every dimension named makes all of it better at once, which is not true of a tool that reasons over whatever it can reach today.
Sketch, illustrative: coverage of what decides the deal rises in steps over time, one step each time you connect a source, name a dimension, make a correction or add another quarter, and never falls, against a dashed line for a tool that re-reads every time.
The dealgraph is one structured, auditable record of everything that decides a deal. It is built from the sources a sales team already has, across four contexts: your company (declared and versioned), the account, the deal and the rep (observed and append-only). Every fact in it carries its evidence, its source, its time and its coverage, meaning how much of the available input was actually read.
A CRM field holds a value and nothing else. It cannot hold the three statements behind a champion, the two candidates that lost and why, the fact that fourteen of fourteen inputs were read, or that the value was different in July. dealmkr writes the value back to your CRM so nobody has to type it, and keeps everything that makes it defensible in the record underneath. Write-back is a projection, not the original, which is also why the record survives a CRM migration.
Plenty of teams have, and the demo is usually good. What breaks is what happens next. Nothing becomes a durable fact, so asking twice gets two answers. Two sources disagree and the agent picks one, silently, and differently next time. And there is no sense of time, so changing the prompt changes every past answer with nothing recording that it did. Retrieval is not a record. The dealgraph makes each fact durable, resolves conflicts on record, and pins the producer and prompt version to every claim. Gartner found only 48% of AI projects reach production, after eight months on average; the missing piece is nearly always the data underneath.
Open any fact and you see the statements that support it, with speaker and timestamp; the candidates it beat and why each lost; how many inputs were read and how many failed; when it was first observed and when it was superseded; and which producer and prompt version wrote it. Facts are one of four kinds: observed, derived, inferred absent, or unknown, and the record says which.
The record resolves the conflict and writes down the decision. Authority runs in a fixed order: your declared company context first, then your CRM, then facts already persisted from calls, email and documents, then web and enrichment sources only when the internal record cannot answer. The losing candidate is kept, with the reason it lost.
It says so. If budget has never come up across twenty-two inputs, the record holds "budget: not found in 22 inputs" as a fact, and counts it as a gap rather than a pass. An unreadable or unclassified value always fails toward gap present, never toward no gap.
Yes, from the data workbench. A correction is not a ticket. It writes a user-authority claim that supersedes the producer's, permanently, and the record remembers who made it and when. You can also flag a producer so the same mistake is caught across the book.
Company context is what you declare: ICP, personas, buying roles, products, pricing, proof points, competitive position, sales process, stages, exit criteria, playbook rules and policy limits. It is versioned so that an artifact built in October resolves against October pricing even after the price list changes, and so that "champion" means what your playbook says it means, not what a generic model assumes.
Yes, through MCP. The dealgraph is exposed as a tool your agents can call. Ask for a champion by name and get it back typed, with its evidence and its coverage. Your automations, your models, your tokens. Access is by pilot for GTM engineering teams today.
Yes. dealmkr reads your CRM and writes facts back to the fields you choose, so reports keep running and automations keep firing. The record underneath holds what the CRM cannot.
Your CRM (HubSpot or Salesforce), your call recorder, email and calendar, and optionally documents, collaboration tools and external data. Nothing changes for reps: they stay where they work. Connect in an afternoon, see impact in days.
Yes, as a consequence of the record rather than as a separate feature. Your methodology, stages, exit criteria and qualification bar are declared once in company context and applied to every deal. A stage cannot be skipped quietly, and a commit that fails your own criteria is visible before the quarter closes. dealmkr started as a sales execution platform built around MEDDICC; the dealgraph is what makes that enforcement checkable.
Evidence-backed, AI-supported, on purpose. Not because a model improved, but because what you know about your deals finally accumulates instead of evaporating.