Back to home Growvate for Startups · Seed → Series B

We're the AI team you can't hire fast enough.

Funded startups don't have six months to hire senior AI engineers. We embed with your team, ship the production AI your board, investors and users are waiting on, and hand it off when you're ready to take it in-house. Fixed price. 30-day sprints. Senior-only team.

Book a 30-min sprint scope call See how engagements work

40+builds shipped
30 dayskickoff to live
Senior-onlyno juniors
You ownthe code & IP

(01) The story

You raised the round. Now what?

You closed the round. The deck promised AI. Investors are politely asking when they'll see it in the product.

Your founding engineers are heads-down on the core product roadmap that also raised the round. The CTO has 12 tabs open. The recruiter you hired is sending you "senior ML engineer" profiles with $400k expectations and three competing offers.

Six months in, the AI feature is still "Q3 priority". Your competitor — the one with the same deck — just shipped theirs.

This is the gap we close.

We don't replace your team. We're not a co-founder. We're not a vendor with a 16-week discovery phase.

We're senior implementation engineers who plug into your Slack on Monday and ship production AI by week 4. Real code in your GitHub. Real deployment to your infra. Real handoff with eval suites, runbooks and prompt-tuning guides your team can own.

You get the AI feature shipped. Your CTO gets sleep back. Your investors get something to talk about on the next call. Your competitor gets to wonder how you suddenly leapfrogged them.

Then you take it in-house and we move on.

(02) What we ship into startups

Production AI features. Not prototypes.

Each one shipped end-to-end inside a 30-day sprint. Code lives in your repo, runs on your infrastructure, owned by you on day 31.

In-product AI features

Copilots, intelligent search, summarisation, classification, embedded chat — built into your product, in your codebase, in your design system. Includes eval harness and prompt-tuning surface for your team to iterate on.

Customer support agents

Tier-1 ticket triage and auto-resolution with confidence scoring and human-in-the-loop escalation. We've shipped these handling 60–70% of inbound without a human, at production CSAT.

Workflow automation

The internal AI that turns "10 hours a week of operational drag" into "Slack message in your DMs when something needs your attention". Sales ops, customer success, back-office finance flows.

Eval & observability infrastructure

The unsexy 70% of production AI — Langfuse traces, eval suites with real production samples, regression detection, prompt versioning. Built so your team can iterate confidently after handoff.

RAG & data infrastructure

Retrieval-augmented generation over your help docs, internal SOPs, customer data or product knowledge base. pgvector or Pinecone, hybrid retrieval, re-ranking — production-grade, not "works on three docs in a notebook".

Voice & multi-modal agents

For startups where the product is bigger than text — voice agents, image-aware support, document understanding. Whisper + Claude/GPT-4o + your domain logic, deployed and monitored.

(03) Why founders pick us over hiring

Six reasons we're faster than the recruiter.

⚡

Live in 30 days, not 9 months

By the time you've made one senior AI hire, they've negotiated relocation, served notice and onboarded for a month — we've already shipped your first AI feature, monitored it for two weeks and started the next one.

🎯

Senior-only team. No juniors learning on your dime

Every engineer on your engagement has shipped multiple production AI systems. The person pitching you is the person writing the code. No "we'll staff this with a senior plus three juniors and a project manager" agency math.

🔧

Embedded with your team, not "vendor" theatre

We join your Slack. We open PRs against your repos. We attend your standups. Our work shows up in your sprint board. Your team feels like they hired three senior people for a month, not that they engaged a consultancy.

📦

You own everything on day 31

Code in your GitHub org. Deployed to your AWS/GCP/Vercel. Prompts in your repo. Eval suites in your CI. Runbooks in your Notion. We hand off, write the docs, train your team and step back. No vendor lock-in. No "implementation partner forever" arrangement.

💰

Fixed price. Fixed scope. No hourly billing

You see a fixed quote before we start. No surprise invoices, no "we burned through the retainer faster than expected", no incentive to drag work out. If we go over, that's on us.

🏗️

Production-grade from day one

We build with observability, evals, prompt versioning, kill-switches, and rollback baked in. Not "MVP that we'll harden later" — the kind of code that survives contact with real users without your team carrying a pager.

(04) When founders typically call us

Four patterns we see in every quarter.

Series A · SaaS

"Our deck said 'AI-powered' but we have no AI in the product yet."

The situation: Strong product traction, just-closed A round, "AI-native" in the pitch. The engineering team is shipping the core roadmap; the AI feature has slipped two quarters.

What we ship: Your first real in-product AI feature — usually a copilot or intelligent search over the user's own data. 30 days, in your repo, evaluated, monitored, ready for your team to iterate.

Typical outcome: AI feature ships, gets credited in the next investor update, sets a pattern your team uses for everything that comes after.

Seed · B2B

"Support volume is killing us. We can't hire fast enough."

The situation: Product-market fit, growing 30% month-over-month, support load doubling every two months. Hiring more reps is treadmill economics; the founders are answering tickets at midnight.

What we ship: A tier-1 support agent with confidence scoring that auto-resolves the 60–70% of tickets that are the same 12 questions. Escalates with draft answers pre-loaded for the rest. Wired into Intercom / Zendesk / Front.

Typical outcome: Founders out of the inbox by week 4. Support hire postponed indefinitely. Customer CSAT often up, because response time drops to seconds.

Series B · Platform

"We need an AI vertical inside our platform. We don't have the team."

The situation: Series B, mature product, growing into a new motion that requires AI as a strategic moat. The internal team has the platform expertise but lacks LLM-production muscle.

What we ship: The AI vertical end-to-end — model selection, prompt engineering, RAG infrastructure, eval suite, observability, in-product surfaces. Built alongside your team so they own it cleanly.

Typical outcome: The AI vertical ships in one quarter instead of three. Your platform team learns enough by working alongside us to take it forward without us.

Seed-extension · D2C / FinTech / Health

"Customers expect AI now. We don't know what to build first."

The situation: Vertical product where AI is becoming table-stakes (D2C support, FinTech briefs, Health summaries). Founders know they need AI but can't tell which feature is the right first bet.

What we ship: Week 1 audit + ROI-ranked opportunity map → pick the highest-leverage feature → 3 weeks to ship it production-grade. You leave the engagement with both a shipped feature AND a roadmap of next bets.

Typical outcome: Strategy AND execution in 30 days. The kind of clarity you'd otherwise pay a consultancy three months for, plus an actual shipped feature on top.

Featured case study

Operator.io — 21 days, 71% of inbound auto-resolved, ~$340k/year saved.

Series B B2B SaaS with 4,000+ customers and a 6-person support team drowning in tickets. We embedded with their team for 21 days, shipped an autonomous tier-1 support agent built on Claude with confidence-scored escalation, and handed off a system that's resolving 71% of inbound at production CSAT — replacing what would have been a $400k/year support hiring round.

71%tickets auto-resolved
−84%avg response time
21dkickoff to live
$340kannual cost saved

Read the full case study

(05) How engagements work

From "should we talk" to shipped in 30 days.

Day 0

Scope call (30 min, free)

You explain what you want to ship. We tell you, on the call, whether it's a 30-day sprint or a 60-day, what we'd build, what the rough cost is, and whether you'd be better off hiring instead. No follow-up "discovery proposal".
Day 1–3

Audit + spec

We embed for three days. Read your code, sit in on your standups, interview the users we're building for. Output: a one-page spec everyone has signed off — what we're building, the eval criteria, the success metric, what we're explicitly not doing.
Day 4–10

Design + eval suite

Architecture, model selection, retrieval design. The eval suite goes first — 50+ test cases representative of production traffic, so we can measure "is this getting better" before we tune anything.
Day 11–22

Build + ship to staging

The dumb end-to-end on day 11. Iterate against evals daily. Real integrations, real auth, real observability, no mocks. Staging deploy by end of week 3.
Day 23–30

Production rollout + handoff

Shadow mode → limited rollout → full rollout. Your team gets the runbook, the eval CI, the prompt-editing surface and a 60-min handoff session. On day 31 the system is yours.
Day 31+

Optional fractional retainer (your call)

Most clients take the work fully in-house. Some keep us on a few-hours-a-week retainer to monitor evals, iterate on prompts, or scope the next sprint. Either is fine. No lock-in.

(06) Startup founder FAQ

Questions every founder asks before signing.

Do you take equity, or work for equity?

No. We work on fixed-price 30-day sprints, paid in cash. We're not a co-founder. We're the implementation team you'd hire if hiring senior AI engineers in 2026 was actually possible at your stage.

Who owns the code and IP after the engagement?

You do, fully. Standard IP assignment is baked into our SOW. Code lands in your GitHub org, deployed to your infrastructure, under your accounts. When the sprint ends, the work is yours — no licensing, no royalties, no ongoing dependency on us.

Can your team embed in our Slack, GitHub and standups?

Yes — that's the default. Our engineers join your Slack, get GitHub access to the relevant repos, attend your standups, and use your task tracker. Your team should feel like they got 2-3 extra senior engineers for a month, not that they hired a vendor.

How early-stage do you work with?

Sweet spot is post-Seed and Series A/B teams with funding in the bank, a real product, and a specific AI feature or workflow they need to ship. Pre-seed is usually too early for us — you should be hiring a technical co-founder, not engaging an implementation studio.

What happens after the 30-day sprint ends?

Three options. (1) You take it in-house — that's the default; we hand off cleanly with runbooks and eval suites. (2) Optional fractional retainer where we monitor the system and iterate on prompts/evals for a few hours a week. (3) Another sprint on the next AI feature. Most clients pick (1) or (3).

Will you sign an NDA and IP assignment before we share anything?

Yes — standard mutual NDA before any product or roadmap conversation, and standard IP-assignment language in every SOW. Most engagements are signed within a week of the first call.

What if our team is in a different timezone than you?

We work with UK and US-based startups primarily, and structure each engagement to have meaningful daily overlap (4+ hours) with your team's working hours. For Pacific-time and East-coast US teams, we have engineers who keep US-friendly hours.

What's the typical cost?

30-day sprints land between $30k–$80k for most workflow agents and in-product features. Multi-sprint engagements (e.g., shipping an entire AI vertical) can land higher. You get a fixed quote on the scope call before any contract — no surprises.

Can you work alongside our existing engineers?

That's exactly the point. We're an extension of your team, not a replacement. The best engagements are when one or two of your engineers pair with us during the sprint — they learn the patterns, take ownership cleanly, and own the system after we leave.