Ship AI features 3× faster: without hiring a data team.
Your roadmap says AI; your team says hiring takes six months. Our senior squad (587+ products shipped) plugs into yours and gets real AI features into production in weeks.
✓No obligation. In 30 minutes we’ll show you 3 blockers between here and your next fundraise.
587+ products shipped · 6 week avg. first release · 95% extend
587+ products shipped for SaaS, fintech & digital first startups.
Sound familiar?
These are the three problems scale up teams bring us most often. If any of them stings, the intro call will pay for itself.
The AI feature keeps slipping
Every sprint it moves to the next one. Investors are asking, competitors are shipping, and the prototype still isn't production grade.
You can't hire fast enough
Graph engineers, ML ops, prompt engineering: three specialist hires, six months of recruiting, and a burn rate you can't justify pre Series C.
The demo doesn't survive real users
It worked in the pitch. Under real data and real load it hallucinates, times out, and costs a fortune per query.
What week 6 looks like inside your repo
Real pull requests with eval gates in your own CI: not status decks. This is the artifact your investors ask about.
Pull request #214
feat: AI recommendations: v1.0 release
✓evals / hallucination-rate
0.8%: gate: <2%✓evals / latency-p95
320ms: gate: <500ms✓security / dependency-scan
0 critical✓ci / regression-suite
47 scenarios passingweekly releases · failed gate = blocked, not shipped
Scale up · Velocity
Weeks to ship, not quarters.
Hiring a data team to ship one AI feature costs you the feature's entire shelf life. An embedded senior squad ships it instead.
42 days
Architecture to production
0
Engineering hires post launch
587+
Products shipped to date
What we build for scale up teams
Every engagement is scoped from your intro call: these are the systems it usually leads to.
Embedded AI Squad
Senior engineers who join your standups, ship in your repo, and hand over clean: team augmentation without the ramp up tax.
See how embedding works →MVP & AI Product Development
From concept to production ready AI product: architecture, pipelines, evals, and launch, typically 6 weeks to first release.
Explore MVP development →GraphRAG & Agent Infrastructure
The retrieval and agent layer your product needs to be accurate at scale: built once, correctly, on Neo4j.
See the infrastructure →From MVP to Series A
Don’t waste runway on a 6 month hiring spree. We act as your fractional AI engineering department.
1. Discovery sprint
2 weeks. We map your roadmap, understand your data, and architect the solution.
2. MVP build
6 weeks. Production ready code in your repo, on your infrastructure.
3. Knowledge transfer
100% handover. Your team maintains and extends the system after launch.
Hires needed: 0. We are your data team: graph engineers · ML/LLM specialists · data pipeline engineers · DevOps for AI.
AI features in weeks, not quarters.
Production grade releases: first one in six weeks, weekly after that. Your roadmap stops slipping and your investors stop asking.
Your roadmap moves. Your team doesn’t stall.
An embedded senior squad carries the graph, ML and DevOps load while your engineers keep shipping product, and inherit the system through pairing.
Embedded, not outsourced
Your repo, your standups, your CI. Progress you can see in your own tools.
Architected, not improvised
Senior engineers design the boundaries before a line of AI code is written.
Tested, with evidence
Eval suite wired into CI. Failed regression = blocked release, every time.
Handover by design
Docs, runbooks, and pairing from week one. You own it when we leave.
Not “what do you want?” Here’s what works.
We don’t open with a 40 page intake form. Your request lands; senior specialists reply with a concrete shape, drawn from every comparable system we’ve shipped. Recommendations, not interrogation.
Alex Chen · Founder · D2C Wellness Startup
“We needed a personalized recommendation engine, but my small team was stuck building the core app. No budget for a data team: and we needed AI features to close our seed round.”
Advisory · Agentic Giants senior team
Drawn from 587+ shipped products
For a product like yours, we recommend:
- RAG
Lightweight GraphRAG overlay on your existing product database: no data team required.
- SQUAD
Embedded senior squad in your repo and standups from week one; your roadmap keeps moving.
- CI
Eval suite wired into CI hallucination and latency gates; failed = blocked.
- PAIR
100% knowledge transfer your engineers pair throughout and extend it after launch.
Outcome: MVP deployed in 6 weeks: seed round closed with working AI features.
Drawn from our knowledge base of past engagements: the same graph technology we sell.
How it works
Three steps, no sales pressure at any of them. You keep the roadmap whether or not we ever work together.
- 1
30 minute technical intro
We map your roadmap against your current stack and show exactly what's blocking the AI feature from shipping.
- 2
Written findings report
A concrete build plan with timeline and three savings of $50K+: keep it even if we never work together.
- 3
Squad embeds and ships
We work inside your repo and rituals, ship the first release in weeks, and hand over documentation your team owns.
Scale up questions, answered
We work in your repo with your tools: React, Python, Node, Postgres, whatever you already run. The squad adapts to your conventions, your CI, and your review process; you see progress in your own tools, not in slide decks.
Relevant client results
View all case studies →Ready to ship the AI features your investors are asking for?
One 30 minute technical intro. We’ll show you exactly what’s blocking your AI roadmap and deliver a written path to production in weeks: not quarters. The roadmap is yours either way.
✓ No obligation · ✓ Keep the report either way