Ship the AI feature your roadmap promised.
Your roadmap promised an AI feature last quarter. We embed, build it into your product, and ship it to production — support copilots, triage, churn signals, and the AI-native features your users are already asking for.
AI that lives inside your product.
Support is where SaaS AI pays back fastest — so it's where we go deepest. Six places production AI agents move the metrics SaaS teams actually report on.
Support copilots
An agent that drafts and resolves tickets inside your helpdesk — trained on your docs, your tone, your edge cases — and knows when to escalate to a human.
Ticket triage & routing
Classify, prioritize, and route inbound at volume by intent, urgency, and account value, so your team touches only what needs a human.
Churn & expansion signals
Surface at-risk accounts and expansion moments from product and support data before they show up in the dashboard — or the renewal call.
AI-native product features
The copilot, the summarizer, the natural-language query bar your roadmap promised — built into your existing codebase, in production.
Onboarding & activation
Guided, generative onboarding that adapts to each customer and shortens time-to-value.
Internal ops agents
Automate the back-office glue — billing exceptions, data hygiene, reporting — that quietly eats your RevOps, data, and CS teams.
Engineering-led, production-grade.
We build into your product, not beside it.
Agents ship inside your codebase and your stack, behind your auth, against your data model — not a bolt-on dashboard.
Eval-gated, not vibe-shipped.
Every release passes an eval suite tied to your real cases before it touches a customer. We prove accuracy before you trust it.
Built to stay reliable.
Observability, guardrails, fallbacks, and prompt versioning from day one — because a SaaS feature has to survive production load.
Four SaaS builds, with the numbers.
An embedded engineering team, a data stack, and two first products taken to paying customers.
Three embedded engineers lifted sprint velocity 2.3x and shipped 4 stalled features in 3 months — saving $392,400/year vs US hiring.
A 7-week modern data stack eliminated 56 hours/month of manual reporting, cut month-end close from 5 days to 2, and preserved $186K in ARR.
A $48K, 11-week logistics SaaS MVP that reached 14 paying customers and unlocked a $2.1M Series A.
A $72K, 14-week two-sided marketplace build processed $2.4M GMV in year one and powered a $3.8M seed round.
For build price, monthly cost and payback across every project we publish, see what ten client projects cost.
We are not a prompt shop bolting a chatbot onto your marketing site. We are senior engineers who ship AI features into your product, behind your auth, against your data — and stay accountable when they hit real load.
Questions SaaS teams ask
Do you build inside our existing product, or a separate tool?
Inside your product. We embed in your codebase and stack, work behind your auth, and ship features your users see in your app — not a bolt-on dashboard.
How fast can we ship an AI feature?
A scoped, production AI feature typically lands in weeks, not quarters. A Gigabit Agents build puts one high-value workflow live for a flat fee agreed before work starts; the smallest engagement, the $8,000 Quick Win Sprint, ships one workflow in 3 weeks.
What about accuracy and hallucinations?
Every agent is gated by an eval suite tied to your real cases, with guardrails and fallbacks. We measure accuracy before deploy and monitor it after.
Who owns the code?
You do. The system, the eval suite, the prompts, and the documentation are yours.
Should a SaaS company hire AI engineers or embed a team?
It depends on whether you can fill the seats. One Series B HR-tech company had two senior roles open for four months, then embedded three Gigabit engineers at $14,400 a month. First pull requests landed in 5 days and sprint velocity went from 34 to 78 points, at $207,600 a year against $600,000 for the equivalent US hires. Hiring still wins when the work is permanent and the roles can be filled.
What does it cost to build a SaaS MVP?
Two first products on this page: a logistics SaaS MVP cost $48,000 and shipped in 11 weeks, reaching 14 paying customers by month six; a two-sided marketplace cost $72,000 and launched in 14 weeks, reaching $2.4M in gross merchandise value in its first year. Those are two engagements, not a price list.
From the field notes
Let’s ship the feature your users keep asking for.
Bring the workflow. We'll scope what a production AI feature looks like in your stack — and what it costs.