Gushwork raises $9 million and reaches a valuation of $33 million

Round summary

• The startup founded in 2023 closed a seed round of $9 million and was valued at $33 million post-money.
• The deal was led by Susquehanna International Group (SIG) and Lightspeed, with participation from several funds.
• Gushwork says it has surpassed 300 paying customers, with subscriptions starting at $800 per month.
• It reports $1.5 million in ARR and 50% to 80% month-over-month growth after launching its “AI search” product.

Scope note: operating metrics (customers, ARR, growth, traffic, and leads) are presented as communicated by the company and/or its founder in statements collected by TechCrunch.

Gushwork’s initial funding

Gushwork, a startup founded in 2023 by Nayrhit Bhattacharya and Adithya Venkatesh, announced a $9 million seed round at a time when the way of “discovering” companies on the internet is changing. The thesis behind the product —and behind investor interest— is that buyers are starting to look for vendors not only on Google, but also in conversational tools such asChatGPT, Gemini, and Perplexity, which synthesize answers and recommend options.

The round was led by Susquehanna International Group (SIG) and Lightspeed, with participation from B Capital, Seaborne Capital, Beenext, Sparrow Capital, and 2.2 Capital. With this injection, the company raises its total funding to $11 million, according to the startup itself.

The competitive context also helps explain the timing. As AI companies “bite” into traditional search’s share, incumbents are reacting: Google has rolled out AI-generated summaries and conversational features in its search engine. In that reshuffling, Gushwork is trying to position itself as a provider of “agents” that automate marketing aimed at appearing both in classic results and in model-generated answers.

The company is incorporated in Delaware and maintains an office in Bengaluru. Its team totals around 70 employees in India, in addition to several contractors. The stated plan for the new capital is threefold: expand engineering, improve model accuracy, and scale go-to-market.

Product evolution and focus
– July 2023 (pre-seed): a $2.1M round led by Lightspeed.
– 2023–2024 (initial product): a broad focus on helping SMBs outsource workflows by combining AI + human labor.
– Shift to “AI search” (most recent): the team narrows its focus to search-driven marketing after seeing demand for online visibility.
– ~3 months before the announcement (launch): rollout of the product centered on “AI search” and the start of the reported run rate.
– Seed round ($9M): capital earmarked for engineering, model accuracy, and go-to-market.
Practical checkpoint: if the product depends on continuous execution (content, links, measurement), the bottleneck usually shows up in onboarding + delivery capacity; that’s why the use of funds in engineering and GTM is especially relevant.

Gushwork’s post-financing valuation

The seed round values Gushwork at $33 million post-money, a notable jump from its previous valuation. A person familiar with the deal indicated that, after its pre-seed round, the company was valued at around $7.5 million. That pre-seed was $2.1 million and was led by Lightspeed in July 2023.

The change in valuation does not come in theempty: aligns with a strategic pivot. Gushwork was born with a broader proposition—helping small and mid-sized businesses outsource workflows by combining AI and human labor—but it narrowed its focus toward search-driven marketing after spotting growing demand to improve online visibility. In Bhattacharya’s words, the customer “pull” from the search area became hard to ignore.

The valuation is also supported by early signals of traction for its newest product. The company says it launched its “AI search”-focused offering about three months ago and that, since then, it has reached an annualized recurring revenue run rate that is meaningful for its stage.

In the market, the bet is clear: if conversational answers become a new “front page” of the internet, showing up in them—and being cited or recommended—could become a new battleground for marketing. Gushwork is trying to capture that budget with automation, avoiding companies having to rely on large in-house teams.

Interpreting post-money valuations
How to read “$33M post-money” without getting lost:
– Post-money = the company’s value after the round’s money comes in.
– Pre-money (approx.) = post-money − round amount. With $33M post-money and $9M raised, the implied pre-money would be ~$24M (arithmetic approximation).
– Comparison with the previous round: the article mentions ~$7.5M after the pre-seed; the jump suggests the market is paying for (1) a narrative shift (AI search), (2) traction signals (customers/ARR), and (3) expectations that the channel will grow.
– Important nuance: a “post” valuation does not by itself describe terms like preferences, rights, or structure; it’s a price snapshot, not the full contract.

Growth of Gushwork’s customer base

Gushwork says it has signed more than 300 paying customers, with a marked geographic concentration: about 95% are in the United States. The customer base, according to the company, is made up mainly of high-ticket B2B businesses, with a notable presence of service providers, industrial distributors, and contract manufacturers, also mostly in the U.S. market.

The company argues that its product is designed to capture demand where attention is shifting. In its view, buyers are increasingly using AI tools to research suppliers and products, and that changes the funnel: it’s not only about “ranking” ina results page, but rather to appear in a synthesized answer, in a recommendation, or in a summary.

Gushwork claims that, on average, close to 20% of its customers’ web traffic already comes from AI-driven search and chat platforms, but that those sources account for around 40% of inbound leads. Bhattacharya’s interpretation is that these are higher-intent leads, which increases the likelihood of conversion.

The company also says it has a waitlist of more than 800 companies that it plans to start onboarding. If that pipeline materializes, the operational challenge will be maintaining quality and results while scaling onboarding, especially in a product that combines automation with ongoing execution (content, links, lead follow-up).

In an example cited by the founder, a professional services customer reportedly closed between $200,000 and $350,000 in contracts after adopting the platform, although the customer’s name was not disclosed. Beyond that specific case, Gushwork’s narrative is that the shift in discovery is beginning to translate into pipeline growth for several users.

Early traction and results
Traction and mix figures (according to internal data/founder statements):
– 300+ paying customers.
– ~95% of customers in the U.S.
– ~800+ companies on the waitlist.
– Customer mix: high-ticket B2B (services), industrial distributors, and contract manufacturers.
– Channel attribution (reported average): ~20% of traffic from AI/chat platforms, but ~40% of leads.
– Result example (unnamed case): $200k–$350k in contracts closed after adopting the platform.
Reading note: since these are internal metrics and a single unnamed case, they serve as an early signal, not as a guarantee of results for all customers.

Gushwork’s subscription model and pricing

Gushwork operates on a subscription model. The company indicates that its average subscription is around $800 to $900 per month. On an annualized basis, that is equivalent to an approximate annual contract value (ACV) of $9,000 to $10,000, according to Bhattacharya.

The product relies on a network of AI agents that automate typical tasks in search-oriented marketing. Among the functions described by the company are: generating and updating search-optimized content, building backlinks, and following up on leadsincoming through an integrated content management system. In practice, the goal is that the client does not need a large internal team to sustain content production and ongoing optimization.

On the links side, Gushwork claims it typically builds between 10 and 20 backlinks per client, relying on a network of approximately 200 to 300 partner websites. That component is relevant because, historically, link building has been a central SEO lever; Gushwork tries to industrialize it with automation and a preexisting network.

The offering, as the company describes it, aims to cover two fronts at once: visibility in traditional search and presence in AI-generated answers. In an environment where Google is incorporating AI summaries and where tools like ChatGPT or Perplexity are used to research vendors, the promise is to “be where the buyer asks,” not only where they “search.”

The model’s implicit challenge is to prove that performance holds up over time and that automation does not sacrifice accuracy or relevance. That’s why the company says part of the capital will be allocated to improving model accuracy.

Pros and risks of the model
What the “subscription + content + backlinks + measurement” model tends to gain and lose:
– In favor: clear monthly price; outsources ongoing work; can accelerate “time-to-signal” (first leads) if the niche already has demand.
– In favor: if leads from AI/chat have higher intent (as the company claims), ROI may come more from quality than from volume.
– Against / risk: the impact of backlinks and content can vary by industry and due to ranking changes; it’s not a fully controllable channel.
– Against / risk: it requires maintenance (updates, links, tracking); if execution slows, performance can degrade.
– Operational trade-off: automation scales, but it forces you to monitor editorial quality, relevance, and lead attribution so you don’t optimize “vanity metrics.”

Gushwork annual recurring revenue

Gushwork claims it is currently operating at a run rate of $1.5 million in annualized recurring revenue (ARR). That figure comes shortly after a shift in focus and, according to the company, it has since seen enough acceleration to report that ARR level.

The company also announced a short-term goal: to reach between $3 million and $3.5 million in ARR in the next three months. If achieved, it would imply more than doubling the annualized run rate in aquarter, an ambitious goal that depends on two variables: the ability to convert its waitlist and the retention/expansion of current customers under a subscription model.

Gushwork’s read on lead quality is central to justifying ARR and its potential. According to Bhattacharya, although only one-fifth of traffic comes from AI channels, those channels contribute a larger share of leads. In other words: less volume, more intent. In B2B subscription models, that relationship can be decisive in sustaining prices of $800–$900 per month.

In parallel, the discovery market is moving fast. OpenAI reported in July 2025 that ChatGPT was receiving around 2.5 billion prompts a day globally, including approximately 330 million from the U.S. For Gushwork, that scale suggests that conversation with models is becoming a massive research channel.

Keys to interpreting ARR
Quick checklist to interpret the ARR a startup reports:
– Is it real ARR (signed recurring contracts) or an annualized run rate (current MRR × 12)? The text presents it as “annualized recurring revenue”.
– What portion comes from new logos vs. expansion (upsell) vs. price?
– Does the $3–$3.5M target in 3 months imply more customers, higher ARPA, or both? At $800–$900/month, reaching that run rate usually requires more volume and/or better retention.
– What operating assumptions enable it (onboarding, delivery capacity, support)? The waitlist helps, but it doesn’t replace execution.
– Are there signs of churn or average contract length? (They aren’t detailed here; it’s worth asking for them if evaluating the business.)

Gushwork’s monthly growth rate

Gushwork reports that it is growing around 50% to 80% month over month, a figure the company directly links to the launch of its AI-powered search product about three months ago. In early stages, these rates often reflect a combination of a small base, strong initial demand, and delivery capacity; the challenge is sustaining them as customer volume and operational complexity increase.

The company attributes part of its traction to a change in buyer behavior: the use of AI chatbots and browsers to research vendors and products. That change, according to its thesis, alters the marketing “map”: it’s no longer enough to optimize for a traditional ranking; you also have to influence how models “find” and “summarize” information about a company.

Within that framework, Gushwork’s product tries to automate tasks that, otherwise,

would require specialized teams: content production and updates, link building, and lead tracking. The promise of efficiency is key to explaining why an SME or a mid-sized business might pay a relatively high monthly subscription: it outsources a set of ongoing, results-oriented activities.

The company also faces a typical bottleneck of rapid growth: onboarding. With more than 800 companies on the waiting list, the pace of onboarding can become the limiting factor. The company says it will use the capital to scale go-to-market and strengthen engineering, suggesting it is looking to increase delivery capacity while also improving the product.

The competitive environment, moreover, is getting tougher. As Google integrates conversational features and platforms like Perplexity gain visibility, “AI search” stops being a curiosity and becomes a strategic front. For startups like Gushwork, that can mean opportunity—a new marketing budget—but also pressure to demonstrate consistent results.

Interpreting early MoM growth
What “50%–80% MoM” usually means in such an early-stage startup (and what to look at):
– It may be driven by a small initial base; that’s why it’s worth looking at the trend across 2–3 cohorts, not just one month.
– Signals that help sustain it: repeatable onboarding, delivery capacity (content/links/measurement), and retention (customers who renew after the first cycle).
– Typical warning signs: a growing backlog, declining content quality, confusing lead attribution, or excessive dependence on a single channel/platform.
– In subscription products, “real growth” becomes clearer when churn and net revenue retention are reported (or inferred); they are not detailed here.

Transforming the Customer Experience in Telecommunications

The story of Gushwork illustrates a broader pattern: when interaction channels change (conversational search, automation, agents), companies that adapt earlier tend to capture efficiency and growth. In telecommunications and ISPs, that same principle applies to the customer experience (CX): users already expect fast answers, continuity across channels, and frictionless resolution.

The Importance of Automation in Customer Support

In telecom operations, the challenges tend to repeat: high cost per interaction, long response and resolution times, channel fragmentation andlow resolution at first contact. Automation—when well implemented—makes it possible to absorb repetitive inquiries (billing, payments, service status, incidents) and free up human agents for complex cases.

The approach that is becoming most established is the hybrid one: AI to classify, respond, and execute predictable flows; humans for exceptions, oversight, and sensitive decisions. That “human-in-the-loop” prevents automation from becoming a black box and helps sustain quality at scale, especially when there are SLAs and strict operational processes.

Strategies to Improve Customer Retention

In telecom, retention often depends as much on price as on experience. Reducing friction at critical moments—outages, complaints, late payments, reactivations—can directly impact churn. Some operational strategies rely on: true omnichannel support (with shared context), prioritization by reason for contact, and automated journeys for collections and service recovery.

The key is to measure and manage: first response times, resolution times, recontact, and SLA compliance by type of inquiry. When those metrics improve, service perception tends to stabilize, and with it the likelihood of staying.

Connecting CX with Results
Practical framework to connect CX with results in telecom/ISPs:
– Metrics (what to measure): FRT (first response), TTR (resolution), FCR (first-contact resolution), recontact, SLA compliance, CSAT/NPS.
– Levers (what to move): guided self-service, intent-based routing, a living knowledge base, bot↔human handoff with context, payment/reactivation automation.
– Results (what to expect): lower cost per contact, fewer repeat tickets, higher FCR, lower churn due to friction, and better service perception.
– Condition for success: integrations with operational systems (CRM/billing/ticketing) and governance of the “human-in-the-loop” for exceptions.

Transform Your Customer Experience with Suricata Cx

Suricata Cx is an AI-powered omnichannel customer experience platform, designed specifically for ISPs and telecom operators in the Americas and Spain. It combines conversational AI, automation, human-in-the-loop flows, and operational integrations to scale support, sales, and service with control and traceability.

The definitive solution for ISPs and telecom operators

Unlike a generic chatbot, Suricata Cx worksas a CX “operating system” oriented to real telecom flows: billing inquiries, payments, incidents, service status, account data, and intelligent routing to agents. The goal is to reduce costs and time without losing human oversight.

Leverage artificial intelligence to optimize your operation

Suricata Cx automates what’s predictable and assists teams when judgment is needed: pre-classification, unified context, bot↔human handoff, and full auditing. In addition, it enables omnichannel operations on WhatsApp, webchat, social media, and IVR/voice, with a single view for agents and supervisors.

A customer-centric approach that drives measurable results

CX improvement becomes tangible when it’s tied to operational metrics: shorter response time, higher first-contact resolution, fewer recontacts, and better SLA compliance. With integrations to business systems and journey automation (including payments and recovery), the focus shifts from “handling more” to “resolving better,” at scale.

Gushwork raises $9 million and reaches a $33 million valuation, a sign that AI is already redefining how a provider is discovered and chosen. At Suricata Cx we closely follow this shift because, in telecom and ISPs, the same logic applies: gaining visibility and trust in conversational channels requires omnichannel CX operations with automation and human control to sustain quality at scale.

This analysis is built from a CX operations perspective in telecom/ISPs: how automation, the hybrid approach (human-in-the-loop), and integrations with business systems impact metrics such as response/resolution times, recontact, and SLA compliance.

Frictionless Omnichannel CX Implementation
Typical (frictionless) implementation of an omnichannel CX platform in telecom/ISP:
– 1) Discovery (1–2 weeks): map contact reasons (top 10), SLAs, active channels, and systems (CRM/billing/ticketing).
– 2) Integrations (2–6 weeks): connect customer data, service status, billing/payments, and ticket creation/inquiry.
– 3) Controlled pilot (2–4 weeks): launch 2–3 high-volume flows (e.g., bill/payment, service status, incident opening) with human-in-the-loop.
– 4) Measurement and tuning (ongoing): compare FRT/TTR/FCR and recontact vs. baseline; review failed conversations and update the knowledge base.
– 5) Scaling: expand to more channels (WhatsApp/web/voice) and journeys (collections, reactivation, retention) while maintaining auditing and traceability.
Checkpoints: define baseline before the pilot; agree on handoff thresholds tohuman; and validate that agents receive complete context to avoid recontacts.

Customer figures, ARR, growth, and traffic/lead attribution reflect only public information available as of the time of writing. In early stages, these indicators can vary rapidly and depend on execution, retention, and changes in search/AI platforms. Some details (such as full round terms or churn metrics) have not been disclosed and could be updated if new information emerges.