Your roadmap has AI on it. Your hiring plan is the reason it hasn't shipped.

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 makes this sector hard

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.

What we bring

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

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.

Compliance context

Regimes we design to in this sector.

These are frameworks we build and document against. They are a description of how we work, not a claim that Round Digital is certified against them — our certification status is published in full on the roadmap.

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.

Questions

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.

Services for this sector

Cloud · Software · Security · Data

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