Engineering for AI startups
Ship the product while your founders stay on the hard problem.
Systems we integrate with
- Claude & OpenAI APIs
- AWS Bedrock / Azure OpenAI
- Vector databases
- Stripe metered billing
- Auth providers / SAML
- Observability & tracing
- Feature flagging
- Data warehouses
What actually goes wrong in ai startups
Recognisable, specific, and drawn from systems we've worked on rather than an industry report.
Your founders are the only people who can do the core work
Founder time goes into auth, billing, dashboards and onboarding instead of the model and the data. That's the most expensive engineering time in the company spent on solved problems.
The demo works and the product doesn't
A notebook that impresses in a meeting is a long way from multi-tenant software with rate limits, quotas, audit logs and an SLA. That gap is usually underestimated by a factor of several.
Inference cost is unpredictable and margins are unclear
Without per-tenant cost attribution you can't tell which customers are profitable, and usage-based pricing becomes guesswork.
You need enterprise readiness before you have an enterprise team
The first serious customer asks for SSO, audit logs, data residency and a security questionnaire. Those requirements arrive all at once and block the deal.
Hiring senior engineers against big-tech offers
You're competing for the same people as companies with a far larger budget, and every month unstaffed is runway spent.
Systems we've built for ai startups
Not a menu, just a description of the shapes of work that come up repeatedly in this vertical.
- Application layer: auth, multi-tenancy, permissions, admin tooling
- Usage metering, quotas and consumption-based billing
- Inference serving infrastructure with autoscaling and cost controls
- Evaluation harnesses and regression suites for model changes
- Data pipelines for training, fine-tuning and feedback capture
- SDKs, public APIs and developer documentation
- Enterprise requirements: SSO/SAML, audit logs, role-based access
- Customer-facing dashboards and reporting
The requirements that are easier to build in than bolt on
Compliance, auditability and correctness cost far less when they're part of the architecture from the start. Retrofitting them is where budgets go.
Speed without a mess you can't sell
Startups need velocity, but technical due diligence happens at your next round. We move fast on product surface and stay disciplined where an acquirer or investor will look.
Cost attribution from day one
Inference cost tracked per tenant and per feature, so pricing decisions rest on data rather than instinct.
Your IP stays yours
Model weights, training data, prompts and evaluation sets are your defensible assets. We work inside your infrastructure and assign all IP to you.
How ai startups clients usually engage
Dedicated Teams
Vetted nearshore engineers who join your standups, your repo, and your roadmap, and who stay long enough to build real context.
AI & Intelligent Automation
LLM features, document processing, and automation that connect to your real data and hold up under production load.
Software Architecture
Architecture, technology selection, and technical due diligence for systems that have to survive scale and staff turnover.
Tell us about your ai startups project
Thirty minutes with an engineer who can scope it. You'll leave with a view on approach, a budget range, and an honest answer about whether we're the right firm, including when we aren't.