Table of Contents

Tables.so Investment Report

Tables.so Investment Report

Category: B2B sales intelligence, lead enrichment, and AI-assisted prospecting

Company Stage: Early commercial / pre-seed

Founder or Founders: Jens Guldbeck Bjerregaard, Oliver Lyneborg Hvam, and Timotej Kochjar

Headquarters: Aarhus, Denmark

Funding: DKK 300,000 historical angel investment into operating company Theoflow ApS; no later financing publicly disclosed

Business Model: Credit-based B2B SaaS with free, Team, and Enterprise plans; paid pricing is sales-led and not publicly disclosed

Product Hunt Launch Date: September 8, 2026 for the current launch; previous launch February 17, 2026

Report Date: September 11, 2026

Investment Metric	Assessment
Venture Potential	64/100
Unicorn Path	Conditional
Valuation Attractiveness	Not Assessable
Evidence Confidence	58/100
Final Decision	Watch

Executive Summary

Tables.so is a B2B prospecting platform that combines contact search, third-party data enrichment, AI research, lead scoring, LinkedIn prospecting, and CRM synchronization. A user describes an ideal prospect in natural language, and Tables returns relevant people and companies with contact details, qualification research, and cited sources. The intended users are small and mid-sized outbound-sales teams that find products such as Clay or Apollo powerful but operationally complex (official website; Product Hunt).

The product is more substantial than a launch-page prototype. It has a working Chrome extension, a live HubSpot integration, a Claude MCP connector, a free 100-credit allowance, and formal legal and data-processing documents. The Chrome extension had 48 users when reviewed, while Pipedrive and Attio integrations were still described as coming soon (Chrome Web Store; integrations; pricing).

The strongest investment signal is the combination of a three-founder team, a live commercial product, hiring activity, and founder-reported MRR growth of 148% over five weeks. However, the MRR baseline, current revenue, customer count, retention, contract values, and gross margin are not disclosed, so the growth percentage cannot be evaluated in economic context (founder hiring post).

The most important concern is defensibility. Tables markets access to a 300-million-plus-profile database and 98% accuracy, but its privacy documentation says that it does not compile or maintain that contact database itself. Search requests are routed to third-party providers including Prospeo, Findymail, Coresignal, and ContactOut. Consequently, the database is better understood as an aggregated access layer than as a proprietary data asset (privacy policy; sub-processors).

The final decision is Watch. Tables operates in a proven, venture-scale category, and the early growth and hiring signal deserve attention. Formal diligence should wait for verification of ARR, retention, customer references, data costs, and whether the product can build a durable advantage against much larger sales-intelligence and workflow platforms.

Product Overview

Tables addresses the fragmented process of B2B prospecting. Sales-development representatives frequently move between contact databases, professional networks, spreadsheets, research tools, enrichment services, and CRMs. Tables aims to consolidate search, enrichment, qualification, and CRM delivery into one workflow.

The platform offers:

Natural-language search for companies and decision-makers.
Company and contact filtering.
Work-email and mobile-number enrichment.
AI-generated research and qualification columns.
CSV export and shared workspaces.
A Chrome extension that operates alongside LinkedIn.
HubSpot synchronization.
A Claude MCP connection for searching and enriching data from a conversational interface (official website; MCP page).

The company reports more than 300 million profiles, 125 million mobile numbers, 98% field accuracy, and a 30% connection rate. These are company claims and were not independently audited for this report. The claim that records are refreshed every seven days also lacks independent verification (data page).

The pricing model is partly opaque. The free plan includes 100 credits. Team and Enterprise pricing requires a sales conversation. Published consumption rates are 0.5 credits for a company or contact record, one credit for an email, ten credits for a mobile number, and one credit per AI enrichment per row. Customers are not charged when Tables cannot provide a requested mobile number (pricing).

The product replaces manually assembled stacks involving spreadsheets, separate data vendors, browser research, and CRM imports. Its primary benefit is reduced prospect-research time, not a fundamentally new sales channel.

Product Quality Assessment: Credible early product with useful integrations and a clear workflow, but data-quality and customer-outcome claims remain unverified.

Founder and Team Assessment

The operating entity is Theoflow ApS, CVR 45593185, registered at Universitetsbyen 71, Aarhus. Its terms identify the same entity as the contractual provider of Tables (terms).

Jens Bjerregaard is CEO and commercial co-founder. Public profiles show experience in marketing, lead generation, and paid social. Oliver Hvam identifies himself as founding CTO and reports computer-science studies at Aarhus University. Timotej Kochjar is a technical co-founder and reports a data-science background at Aarhus University (Jens Bjerregaard profile; Oliver Hvam profile; Timotej Kochjar profile).

The three founders originally built Theoflow as a CRM and later as an agency-onboarding product. A founder interview acknowledged that the initial broad CRM lacked usage traction, leading the team to narrow its focus. The company subsequently pivoted again into Tables, indicating speed and willingness to abandon weak assumptions, but also creating product-strategy risk (TechSavvy).

The same article reported a DKK 300,000 investment from Jeff B. Blaavand for 10% of Theoflow, implying a DKK 3 million post-money valuation at that historical financing. The current cap table and whether subsequent securities were issued are not public.

LinkedIn lists three associated employees, while a Danish company-directory snapshot reported one employee. These measures use different methodologies and dates; neither should be treated as authoritative current headcount. The founder identities are clear, but sales staffing and broader organizational depth are not.

Founder Assessment: Balanced commercial and technical founding team with demonstrated adaptability, but limited operating history and repeated pivots increase execution risk.

Market Opportunity

The narrow initial market is European and North American SMB and mid-market sales teams conducting structured outbound prospecting but lacking dedicated revenue-operations engineers. These customers already pay for lead data, enrichment, CRM software, and prospecting automation, establishing willingness to pay.

Because Tables does not publish Team pricing or customer numbers, bottom-up sizing requires explicit assumptions. If the realistically reachable segment contains 25,000–100,000 sales teams and average annual contract value is $2,000–$10,000, the implied revenue pool is approximately $50 million–$1 billion annually. This is an analyst scenario, not a verified market estimate.

The assumed ACV range is directionally supported by adjacent pricing. Clay’s current Launch and Growth plans begin at $167 and $446 per month on annual billing, while enterprise pricing is customized (Clay pricing). Tables could achieve higher ACV through additional seats, credits, CRM enrichment, data-as-a-service, APIs, intent signals, or enterprise governance.

The category can support large outcomes. Apollo reports use by sellers at more than 600,000 companies, while ZoomInfo reports more than 30,000 customers (Apollo pricing; ZoomInfo pricing). Clay’s September 2026 financing valued it at $7.1 billion, demonstrating investor demand for AI-enabled go-to-market infrastructure, although Clay is substantially more mature and should not be treated as a direct valuation comparable for Tables (Reuters).

Traction and Growth Signals

Tables’ first Product Hunt launch on February 17, 2026 earned six points, three comments, and a daily rank of 52. The September 8 relaunch ranked tenth and the overall product page showed 120 followers and no reviews (first launch; September leaderboard). This indicates modest launch awareness, not product-market fit.

More relevant signals include:

Founder-reported MRR growth of 148% over five weeks, without a disclosed starting or ending value.
Recruitment for sales-development staff in Aarhus.
Three people associated with the company on LinkedIn.
A Chrome extension with 48 users and an update dated May 17, 2026.
Recurring product announcements between May and August 2026.
A previous Theoflow version that reportedly acquired initial paying agency customers before the later pivot.

The earlier paying customers used an agency-onboarding product and should not be counted as Tables customers. No independently verified Tables customer count, revenue, cohort retention, case study, or enterprise contract was found.

Traction Assessment: Some credible commercial-motion signals, but the scale and durability of traction remain unverified.

Competitive Position

Direct competitors include Clay, Apollo, ZoomInfo, Cognism, and other lead-database and enrichment providers. Manual alternatives include LinkedIn research, spreadsheets, separate email-finding tools, and CRM imports.

Tables’ current differentiation is simplicity. It packages data providers and AI research behind a salesperson-oriented interface rather than requiring users to construct complex workflows. It also emphasizes source visibility, technology-stack filtering, HubSpot delivery, and per-result credit charging.

However, the product’s underlying data comes from Prospeo, Findymail, Coresignal, and ContactOut. OpenAI and Anthropic provide AI processing, while Wappalyzer supplies technology-stack information. This accelerates product development but creates supplier dependence and limits proprietary-data defensibility (sub-processors; Tables versus Clay).

Switching costs are currently low. Export to CSV makes data portable, only HubSpot synchronization is live, and no proprietary network effect is evident. Clay offers workflows across more than 150 providers and extensive CRM, warehouse, signal, and orchestration capabilities, while Apollo bundles contact data with sequencing, calling, and pipeline tools (Clay pricing; Apollo pricing).

If Clay or Apollo matched Tables’ simplified interface within six months, customers might retain Tables because of lower cost, local European support, faster onboarding, or better Nordic data. None of these advantages is yet quantitatively demonstrated.

Defensibility Assessment: Low

Business Model and Economics

Tables combines subscription software with credit consumption. This can support expansion revenue as customers retrieve more emails, phone numbers, and AI research. However, paid plan prices and included credit volumes are not public, preventing calculation of ACV or unit economics.

The company incurs variable costs for four external contact-data providers, AI processing, Wappalyzer, cloud hosting, background jobs, analytics, CRM synchronization, and payment processing. Mobile numbers consume ten times as many credits as emails, implying materially higher acquisition cost, but supplier pricing and markup are unknown.

Gross margin could be below conventional SaaS levels because a meaningful portion of revenue pays third-party data providers. Scale could improve purchasing terms, but larger competitors likely possess stronger volume economics. Sales-led pricing and onboarding also add customer acquisition and support costs.

The key diligence metric is contribution margin per credit after data, AI, infrastructure, failed lookups, and support—not gross subscription revenue alone.

Unicorn Path

Assume a 10× ARR multiple for a high-growth sales-intelligence SaaS company with strong net retention and acceptable data-adjusted gross margin. Required ARR would be:

$1 billion ÷ 10 = $100 million ARR.

Because Tables does not disclose pricing, customer requirements must use scenarios:

At $2,000 ACV: 50,000 paying organizations.
At $5,000 ACV: 20,000 paying organizations.
At $10,000 ACV: 10,000 paying organizations.
At $25,000 enterprise ACV: 4,000 customers.

Achieving this scale requires international expansion, more CRM integrations, enterprise security controls, API and bulk-data products, repeatable inbound or partner distribution, and stronger proprietary data or workflow advantages. Gross margin would likely need to exceed approximately 70% to justify a premium SaaS multiple; current margin is unknown.

The market supports billion-dollar companies, but Tables’ present product and supplier-dependent data layer do not yet establish a credible standalone path without substantial expansion.

Unicorn Path: Conditional

Valuation Assessment

The only disclosed transaction is the historical DKK 300,000 investment for 10% of Theoflow, implying a DKK 3 million post-money valuation. That transaction preceded the current Tables product and cannot responsibly establish current value.

Current ARR, financing terms, valuation, ownership changes, debt, SAFEs, and fundraising status are not publicly disclosed.

Valuation Attractiveness: Not Assessable

Assessment requires current ARR, monthly growth, retention, gross and contribution margins, burn, runway, round size, post-money valuation, investor rights, and the current cap table.

Key Risks

Unverified commercial scale: Growth is reported without an MRR baseline.
Low data defensibility: Contact records come from external providers.
Powerful incumbents: Clay, Apollo, ZoomInfo, and Cognism have broader products and distribution.
Data gross-margin pressure: Each search may incur provider and AI costs.
Regulatory exposure: B2B contact data and unsolicited outreach create GDPR and national marketing-law obligations.
Weak switching costs: Data can be exported, and CRM integration coverage is limited.
Repeated pivots: The company moved from CRM to onboarding and then prospecting.
Small team: Product, sales, compliance, and support depend on three founders.
Unverified accuracy claims: The advertised 98% accuracy has not been independently substantiated.
Supplier concentration: Changes in provider pricing or access could impair the product.

Final Assessment

Venture Potential: 64/100

Category	Score
Market Size and Expansion Potential	18/20
Traction and Growth Evidence	9/20
Founder and Team	11/15
Product Strength	8/10
Distribution Potential	8/15
Business Model and Economics	6/10
Defensibility	4/10
Total	64/100

The category, product breadth, founder team, and reported near-term revenue acceleration are positives. Weak data ownership, opaque pricing, unverified retention, and formidable competition prevent a stronger score.

Evidence Confidence: 58/100

The legal entity, founders, historical financing, product functionality, data suppliers, privacy structure, integrations, and Product Hunt history are documented. Revenue growth is founder-reported. ARR, customer count, retention, ACV, gross margin, CAC, burn, runway, cap table, and current valuation remain unavailable.

Final Decision: Watch

Tables is close to qualifying for diligence, but the public evidence does not yet demonstrate sufficiently material or durable commercial traction. The company should be monitored for verified revenue scale, retention, contribution margins, and evidence that its simplicity advantage produces lower acquisition cost or higher adoption than established competitors.

Upgrade Conditions

Verified ARR of at least $1 million.
More than 70% six-month customer retention.
Net revenue retention above 100%.
Data-adjusted gross margin above 70%.
At least 100 paying teams or a smaller number of meaningful enterprise contracts.
Evidence that the 148% MRR increase came from retained customers rather than a very small base.
Repeatable customer acquisition outside founder-led sales.
Independent validation of contact accuracy and connection rates.
A durable advantage in European data, workflow integration, or proprietary intent signals.

Downgrade Conditions

Growth falling sharply after the launch period.
High churn following initial data-credit consumption.
Data-provider costs preventing attractive contribution margins.
Loss of a major enrichment supplier.
Regulatory complaints or failures in objection and suppression handling.
Incumbents matching the simplified workflow without a price disadvantage.
Further major product pivots before establishing retention.

Questions for Further Diligence

What are current ARR, MRR, and absolute MRR figures behind the reported 148% increase?
How many paying organizations and active monthly users does Tables have?
What are 30-, 90-, and 180-day logo and revenue retention?
What are average ACV and credit consumption by customer segment?
What is contribution margin after data-provider, AI, hosting, and support costs?
How is the reported 98% accuracy measured, and has it been independently audited?
What percentage of searches produce a usable email, mobile number, and qualified prospect?
Which acquisition channels drive customers, and what is CAC by channel?
What are the current founder ownership percentages following the historical angel investment?
What are burn, runway, hiring plan, and current fundraising terms?
How quickly could Tables replace a provider that changes pricing or terminates access?
What proprietary data or workflow advantage can compound beyond supplier aggregation?