SAAS & TECHNOLOGY
Your roadmap has AI on it. Your hiring plan is the reason it hasn't shipped.
Embedded copilots, RAG search and agentic features inside your product — fast, with dedicated engineering pods when your roadmap outgrows your headcount.
AI features that ship
Software companies rarely lack the idea. They lack the specialist capacity to build it while the existing roadmap keeps moving — and the market for people who have actually shipped production AI is the tightest in technology. So the copilot stays in the backlog, the demo impresses the board, and a competitor ships first.
We close that gap from either direction: we build the feature with you, or we embed the engineers who build it with your team. Both start the same way — with the smallest slice that proves the feature is worth its inference cost.
What we hear from SaaS & Technology leaders
AI features that demo but don't ship
Prototypes that impress in a meeting and collapse against real users, real latency and real edge cases.
Specialist roles you can't hire against
ML and agent engineers competing with every funded company in the market, on a timeline your roadmap doesn't have.
Inference costs that scale wrong
Features that are viable at pilot volume and ruinous at production volume.
Multi-tenancy and AI meeting badly
Customer data isolation that has to hold when a model sits in the middle of it.
TECHNOLOGY WE BUILD
What we build for software companies
Product AI built to ship: evaluated against real usage before launch, instrumented after it, and costed at production volume rather than pilot volume.
- Embedded copilots and in-product assistants wired into your domain model, not bolted onto the side
- RAG and semantic search over customer data with tenant isolation enforced at the retrieval layer
- Agentic features that take real actions in your product, with guardrails and audit trails
- Evaluation harnesses and observability so quality is measured rather than assumed
- Inference cost optimization — routing, caching and model selection against your actual traffic
- Platform engineering and multi-tenant architecture for scale
TALENT WE SOURCE
Who we source for software companies
Senior engineering capacity that slots into your codebase, your tools and your rhythm — individually, or as a pod with a lead and single-point accountability.
- ML engineers, agent developers and applied AI specialists
- Senior full-stack, backend and platform engineers
- Cloud architects, DevOps and SRE
- Data engineers and analytics engineers
- Dedicated pods — lead, engineers and QA under one accountability line
STANDARDS WE WORK TO IN SAAS & TECHNOLOGY
What changes
The feature ships
Evaluated against real usage before launch instead of discovered by customers after it.
Capacity without a twelve-month hiring cycle
Senior engineers embedded in weeks, scaled up or down monthly.
Unit economics that survive scale
Inference cost modelled at production volume before the feature reaches production.
SaaS & Technology questions we get asked
Can you work inside our existing codebase and process?
Yes — that's the normal case. Embedded engineers use your repo, your review process, your tooling and your standup. We're not running a parallel project on the side and delivering a surprise at the end.
How do you keep customer data isolated in a RAG system?
Isolation is enforced at the retrieval layer rather than trusted to the prompt — tenant scoping on every query, with tests that specifically try to cross the boundary. Prompt-level instructions are not a security control and we don't treat them as one.
What if inference costs make the feature uneconomic?
Then we'd rather establish that in discovery than after launch. We model cost at production volume up front, and routing, caching and model selection are design decisions rather than an optimization pass bolted on when the bill arrives.
Individual engineers or a full pod?
Either. Individuals suit a team that has the lead and needs hands; a pod suits a workstream you want owned end to end with one accountability line. Most clients start with one or two engineers and scale from there once the fit is proven.
Working in SaaS & Technology?
One call with a senior engineer — or our talent lead. You'll leave with a plan either way.
Book a call