Doris Labs

An AI-powered agent that connects sales conversations, emails, documents, and CRM data to advance deals.

Cover Block

Publicly reported

Field	Detail
Name	Doris Labs
Tagline	An AI-powered agent that connects sales conversations, emails, documents, and CRM data to advance deals. [dorislabs.com, retrieved 2025]
Headquarters	London, United Kingdom [dorislabs.com, retrieved 2025]
Founded	2025 [LinkedIn, retrieved 2025]
Stage	Pre-Seed
Business Model	SaaS
Industry	Other
Technology	AI / Machine Learning
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Co-Founders (2), Frazer Kearl and Hayden Munt [LinkedIn, retrieved 2025]; [Hayden Munt / LinkedIn, November 2025]

Summary and Signal

PUBLIC Doris Labs is an early-stage sales software startup building an AI agent that connects conversations, emails, documents, and CRM records into a structured view of each deal, a pitch that merits attention because the company is trying to move from meeting notes into workflow control at the point where sales teams feel the most friction [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025]. The company is based in London, was founded in 2025, and is publicly associated with co-founders Frazer Kearl and Hayden Munt, with Munt stating in November 2025 that initial clients were already using the product, although no customer names or performance metrics have been publicly verified [LinkedIn, retrieved 2025] [Hayden Munt / LinkedIn, November 2025].

The product claim is more ambitious than simple call summarization: Doris says it reads calls, emails, and documents, links them to the relevant deal, maintains a typed record of what is true about that deal, and automates follow-up tasks such as drafting emails, building decks, and updating the CRM [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025]. Its public materials also frame the underlying system as a custom ontology or programmable graph of commitments, stakeholders, and strategy, which suggests the differentiation rests on structured sales context rather than on transcript generation alone [LinkedIn, retrieved 2025] [dorislabs.com, retrieved 2025].

On team quality, the clearest public signal is product relevance: Kearl's LinkedIn profile lists prior work as Senior Product Manager, AI, at Patsnap, where he says he led Eureka Materials from pre-release to $1 million ARR and managed on-premises deployments, while Munt's public record ties him to product marketing work at Vertice and to Doris's external product positioning [LinkedIn, retrieved 2025] [vertice.one, retrieved 2026] [Hayden Munt / LinkedIn, November 2025]. Doris's LinkedIn page lists the company at 1 to 10 employees, which is consistent with a pre-seed build phase rather than a scaled go-to-market motion [LinkedIn, retrieved 2025].

The business model appears to be SaaS, but public evidence on pricing, ACV, funding, and investors is still thin: no confirmed venture round, lead investor, or valuation was surfaced in the available materials [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025]. Over the next 12 to 18 months, the key items to watch are whether Doris can convert early product storytelling into named customer proof, repeatable integrations into the major systems of record, and evidence that its ontology-based approach improves deal progression rather than merely making sales follow-up faster [dorislabs.com, retrieved 2025] [Hayden Munt / LinkedIn, November 2025].

No independent source found -- This section relies primarily on company-owned materials and founder LinkedIn posts, with limited independent public corroboration.

Taxonomy Snapshot

Axis	Value
Stage	Pre-Seed
Business Model	SaaS
Industry / Vertical	Other
Technology Type	AI / Machine Learning
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Co-Founders (2)

Company Overview

PUBLIC Doris Labs appears to be at the very start of company formation rather than a business already visible through financing or press. The company presents itself as an AI sales software startup based in London, and its public materials describe a product that connects sales conversations, emails, documents, and CRM context to help advance deals [dorislabs.com]. The company website and LinkedIn company page both indicate a 2025 founding, while founder materials point to a public launch in November 2025 [dorislabs.com] [LinkedIn, retrieved 2025] [Hayden Munt / LinkedIn, November 2025].

The clearest milestone sequence visible from public records is short but coherent. Doris Labs was founded in 2025 according to its website and LinkedIn company page, and on November 6, 2025, co-founder Hayden Munt publicly introduced the company and said it had initial clients running on the product [dorislabs.com] [LinkedIn, retrieved 2025] [Hayden Munt / LinkedIn, November 2025]. A subsequent website snapshot shows the company marketing "Sidekicks" and inviting users to join a waitlist ahead of a stated November 5 product arrival, which is consistent with a very early commercialization phase rather than broad public rollout [dorislabs.com].

Public materials reviewed for this section do not identify a legal entity name, filing jurisdiction, or financing milestone. Nor do they disclose investors, valuation, or accelerator participation on the company website [dorislabs.com]. That leaves the company overview grounded mainly in first-party web presence and founder announcement, with chronology clearer than capitalization.

No independent source found -- Based primarily on company website and founder LinkedIn materials, with no state filing or independent database citation used in this section.

The Product and the Stack

MIXED The product case is straightforward enough on the surface: Doris says it sits across the sprawl of sales work, reads calls, emails, and documents, links them to the relevant opportunity, and maintains a structured record of what is true about that deal [dorislabs.com, retrieved 2025]. On its website, the company frames this as a sidekick for each deal, with visible examples that span meeting prep, follow-up drafting, commitment tracking, and reminders when threads go quiet [dorislabs.com, retrieved 2025]. LinkedIn materials describe the same core motion in slightly more operational terms, saying Doris connects data from notetakers, CRMs, emails, and messages into a custom ontology, then automates work such as drafting follow-ups, building decks, and updating the CRM [LinkedIn, retrieved 2025].

A more interesting claim is the company's attempt to move beyond note-taking into a persistent system of sales context. Hayden Munt's November 2025 launch post argues that the wedge is turning customer conversations into operational sales intelligence, with the aim of scaling the behaviors of top-performing representatives and improving ramping and upskilling [Hayden Munt / LinkedIn, November 2025]. The company blog pushes that idea further, describing a "programmable graph" of meetings, commitments, stakeholders, and strategy, and stating that the system can derive ideal-customer profiles from won deals and run daily messaging experiments [dorislabs.com, retrieved 2025]. Taken together, the public materials point to a workflow layer that combines deal memory, sales coaching, and CRM hygiene, although those claims remain company-described rather than independently validated [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025] [Hayden Munt / LinkedIn, November 2025].

No independent source found -- Based primarily on company website, company blog, and founder LinkedIn materials, without independent product verification.

The Market They Are Entering

Market context

PUBLIC The market matters now because sales teams have more customer data than before, but much of it still sits in calls, inboxes, documents, and CRM fields that do not reconcile cleanly into a usable record of deal reality, which is the problem Doris says it is addressing [dorislabs.com, 2025] [LinkedIn, 2025]. That framing places the company at the intersection of sales software, conversation intelligence, revenue intelligence, and workflow automation, but the available public evidence does not include a named third-party market report that would support a defensible TAM, SAM, or SOM estimate for Doris specifically.

Given that constraint, the most supportable public read is comparative rather than numerical. Doris presents itself as an AI agent for deal progression, connecting "calls, emails, documents, and deal context" into a structured record and automating follow-up work [dorislabs.com, 2025] [LinkedIn, 2025]. That positions demand around a familiar enterprise pain point: CRM systems hold declared pipeline state, while the underlying evidence of what buyers said, promised, or objected to often lives elsewhere. If that gap persists, tools that convert unstructured sales activity into system-level context should continue to attract attention from B2B revenue teams, even if this report cannot size the category with precision from named independent market data.

Market lens	Public evidence	Relevance to Doris
Conversation intelligence	Doris says it reads calls, emails, and documents and links them to the relevant deal [dorislabs.com, 2025]	Suggests adjacency to call analysis and post-meeting workflow products
Revenue intelligence	Doris describes a typed record of what is true about a deal [dorislabs.com, 2025]	Suggests adjacency to systems that structure pipeline and forecast inputs
Sales automation	Doris says it drafts follow-ups, builds decks, and updates CRM records [LinkedIn, 2025]	Suggests adjacency to workflow and seller productivity tooling
Enablement and coaching	Hayden Munt says the product uses conversations to scale top-rep behavior and improve ramping and upskilling [Hayden Munt / LinkedIn, November 2025]	Suggests adjacency to sales enablement and rep development budgets

The table shows why clean market boundaries are difficult here. Doris is not described publicly as a single-point note taker or a conventional CRM layer. The product story spans several adjacent software budgets, which may widen the opportunity if the workflow lands, but it can also make budgeting, ROI ownership, and competitive positioning harder in the early go-to-market motion.

Demand drivers, substitutes, and external forces

PUBLIC The clearest demand driver in the public record is operational pressure on sales teams to do more with existing headcount. Doris's own materials emphasize advancing deals through better follow-up, preserving commitments, and turning prior deal knowledge into repeatable execution, while Munt's public launch post ties the product to rep upskilling and faster ramping [dorislabs.com, 2025] [dorislabs.com/blog/, 2025] [Hayden Munt / LinkedIn, November 2025]. Even allowing for the fact that these are company-originated claims, they line up with a broader software buying pattern in which teams want AI to reduce manual CRM hygiene and recover information lost in fragmented communication channels.

The substitute set is broad. A sales leader could address the same underlying problem with a mix of CRM customization, meeting transcription products, sales engagement tools, internal enablement processes, and analyst-led deal reviews rather than buying a new system of record for deal meaning. That matters because Doris's public positioning goes beyond summarization into a "programmable graph" of meetings, commitments, stakeholders, and strategy [dorislabs.com/blog/, 2025]. The more expansive that promise becomes, the more the company will be compared not only with AI meeting tools, but also with revenue intelligence and process orchestration products.

Macro and regulatory forces are present, even if the company has not detailed them publicly. Any product that ingests sales conversations, emails, and documents will face routine enterprise questions around data permissions, retention, model usage, and cross-system access controls, particularly for customers operating in the UK and Europe where data governance scrutiny is generally higher. No public source reviewed here describes Doris's compliance posture, deployment model, or data handling architecture, so the market opportunity should be read alongside an adoption constraint: in this category, technical capability and buyer trust tend to scale together, and the second usually takes longer to establish than the first.

No independent source found -- This section relies primarily on company website and LinkedIn materials, with no named independent third-party market sizing source available in the provided research.

The Competitive Field

MIXED Doris Labs is positioning itself less against a single direct rival than against a stack of sales tools that already touch the same workflow, from call intelligence and CRM systems to enablement software and internal manual processes [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025].

The public record here is thin, so the cleanest way to read the landscape is by workflow rather than by confirmed head-to-head competitor list. On one side sit incumbents that already store deal data or system activity, chiefly CRM platforms, which remain the default record of account and pipeline history by definition of the category, even though Doris says its value is creating a typed record of what is true about a deal across calls, emails, documents, and follow-up actions [dorislabs.com, retrieved 2025]. On another side sit conversation and note-taking products that capture meetings and summarize them, which appears to be the reference point Doris is pushing away from when Hayden Munt described the wedge as turning customer conversations into operational sales intelligence rather than summarization alone [Hayden Munt / LinkedIn, November 2025]. A third bucket is the adjacent substitute set inside revenue teams themselves: sales managers, revops staff, and reps still stitching together follow-ups, deck prep, and CRM updates manually, which Doris says it can automate after linking data from notetakers, CRMs, emails, and messages into a custom ontology [LinkedIn, retrieved 2025].

The edge Doris can plausibly claim today is conceptual rather than structural. Its product language is unusually explicit about the object it wants to own, a programmable graph or ontology of meetings, commitments, stakeholders, strategy, and deal truth, not merely transcript storage or email drafting [dorislabs.com/blog/, retrieved 2025] [dorislabs.com, retrieved 2025]. That matters because if the product really becomes the operating layer for what a team believes about a deal, usage could deepen beyond a single meeting surface into prep, follow-up, coaching, and experimentation. The difficulty is that this edge is still perishable in public evidence: the sources do not show named customers, proprietary data rights, exclusive integrations, funding scale, or a channel advantage that would make the position hard to copy [Hayden Munt / LinkedIn, November 2025] [dorislabs.com, retrieved 2025].

The company is most exposed where existing systems already have distribution and permission. CRM incumbents already own the official database for pipeline management, and conversation intelligence vendors already own meeting capture in many sales organizations, so Doris has to persuade teams to trust a new source of truth rather than a helpful overlay [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025]. That is a nontrivial go-to-market burden for a company whose public footprint, at least so far, shows 1 to 10 employees, no confirmed funding announcement, no named customers, and no verified ecosystem partners [LinkedIn, retrieved 2025] [dorislabs.com, retrieved 2025]. The category risk is straightforward: if incumbents can add ontology-like deal reasoning inside products buyers already pay for, Doris could be pushed into a feature comparison before it has built distribution density.

The most plausible 18-month scenario is a sorting process between systems of record, systems of capture, and systems of reasoning. Doris has a path if revenue teams decide that summarization is no longer enough and want software that can convert fragmented deal exhaust into operational guidance, particularly around commitments, coaching, and next actions, which is how the company is currently framing the product [Hayden Munt / LinkedIn, November 2025] [dorislabs.com/blog/, retrieved 2025]. In that scenario, the likely winner if execution holds is Doris itself, but only if it can turn the ontology claim into visible proof points such as named deployments or repeatable workflow adoption. The likely loser if buyers remain satisfied with existing stacks is the standalone reasoning layer without entrenched distribution, which in this case would leave Doris vulnerable to CRM and conversation-intelligence vendors that can bundle adjacent functionality into established seats.

No independent source found -- This section relies primarily on company website and LinkedIn materials, with no named public competitors confirmed in the source set and no independent third-party market reporting used for head-to-head validation.

Opportunity

PUBLIC

The prize here is large if Doris can become the operating layer that sales teams trust to turn fragmented deal activity into repeatable revenue execution, a role that sits closer to system-of-record value than to a standalone meeting assistant [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025].

The headline opportunity is straightforward. Doris is not presenting itself as a tool that merely records calls or drafts notes. Its public materials describe a product that reads calls, emails, and documents, ties them to a deal, and maintains a typed record of what is true about that deal, while also automating follow-up work such as drafting emails, building decks, and updating the CRM [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025]. If that product works as described, the company could grow into a control point for frontline sales execution: the layer that interprets buyer intent, tracks commitments, and pushes the next action across the rest of the stack. That outcome is reachable, not just aspirational, because the founders are positioning around workflow and deal progression rather than transcription alone, and because Hayden Munt stated in November 2025 that initial clients were already up and running [Hayden Munt / LinkedIn, November 2025].

A useful way to frame the upside is through the specific paths by which a narrow sales assistant can become a broader revenue platform.

Scenario	What happens	Catalyst	Why it's plausible
Workflow wedge into revenue OS	Doris starts with follow-up automation and deal understanding, then becomes the daily system sales teams use to track commitments, prep meetings, and coordinate stakeholders across every active opportunity	Product adoption sticks around high-frequency workflows such as follow-ups, meeting prep, and CRM updates [LinkedIn, retrieved 2025] [dorislabs.com, retrieved 2025]	Public materials already show Doris spanning calls, emails, documents, CRM data, and follow-up actions rather than a single surface [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025]
Conversation data becomes sales intelligence	Doris uses deal-level history to identify what top representatives do differently, then productizes those patterns into coaching, ramping, and messaging recommendations	Early customers validate that conversation-derived insights improve rep ramping and execution quality [Hayden Munt / LinkedIn, November 2025]	Munt's launch post explicitly frames the product around scaling the behaviors of top-performing representatives and improving upskilling and ramping [Hayden Munt / LinkedIn, November 2025]
Programmable graph becomes platform	Doris turns its internal deal graph into a programmable layer for segmentation, ICP generation, and daily messaging experiments, expanding from seller productivity into revenue operations and GTM planning	The ontology and graph model prove flexible enough to support multiple downstream workflows beyond note capture [dorislabs.com/blog/, retrieved 2025] [LinkedIn, retrieved 2025]	The company already markets a “programmable graph” of meetings, commitments, stakeholders, and strategy, and says it can derive ideal-customer profiles from won deals and run daily messaging experiments [dorislabs.com/blog/, retrieved 2025]

The compounding mechanism would come from data structure, not just seat count. Doris says it connects notetakers, CRMs, emails, and messages into a custom ontology, and its website frames that layer as a persistent understanding of the deal rather than a one-off summary [LinkedIn, retrieved 2025] [dorislabs.com, retrieved 2025]. If that ontology improves with every conversation, document, and follow-up action tied back to outcome, each deployed account should get harder to replace over time. The more a team trusts Doris to capture commitments, prep meetings, and shape outbound responses, the more the product moves from assistant to embedded workflow infrastructure. Public evidence is still thin, but the claim of initial clients already operating on the platform suggests the flywheel is at least beginning in live environments rather than in a pre-launch concept phase [Hayden Munt / LinkedIn, November 2025].

The size of the win is harder to anchor precisely because there is no cited third-party market report or public comparable in the available source set. Even so, the upside case can be stated in functional terms. If Doris were to become the default intelligence layer for mid-market and enterprise revenue teams, the company would likely resemble a strategic system in the sales software budget rather than a point AI feature, which implies materially larger contract potential and stronger retention than a standalone call summary product [dorislabs.com, retrieved 2025] [LinkedIn, retrieved 2025]. That translates into a credible path to venture-scale outcomes if the workflow wedge, ontology model, and early customer proof all hold. Any valuation framing here would be scenario, not a forecast, and investors would need external market benchmarks before underwriting a more numerical upside case.

No independent source found -- This section relies primarily on company website, company blog, and founder or company LinkedIn materials, with no independent named-publisher reporting or third-party market data in the cited evidence.

Sources