Software development for scale-ups
Our scale-up software development adds delivery capacity alongside your hiring plans: a small team that picks up a product stream, an integration backlog or platform work inside your codebase. Our engineers use coding agents under review, so a small team covers more ground while your core team keeps the main roadmap moving.
Where the work gets stuck.
A roadmap bigger than the team
After a funding round the roadmap grows faster than hiring, and experienced engineers take months to recruit. Commitments to customers and investors start to slip.
Integrations that interrupt the product
Enterprise deals come with requests for SSO, SCIM provisioning, Salesforce sync and data exports. Each one pulls core engineers off the product.
Platform debt catching up
The architecture that reached product-market fit struggles with larger tenants, enterprise security reviews and new regions. Migrations and test coverage keep moving to next quarter.
How we improve scale-ups.
Each one is scoped around your systems and rules, and each one keeps a person in charge of the decisions that matter.
New product module or app
We build a defined part of the roadmap, such as a new module, a mobile app or a public API, from design to release within your architecture. Your product manager sets priorities, and your tech lead approves the design.
Enterprise integrations
SSO, SCIM provisioning, Salesforce and HubSpot sync, webhooks and public API endpoints built to your standards and documented for customers. Your core team stays on the product.
Platform hardening and migrations
Multi-tenancy changes, framework upgrades, database migrations and missing test suites, done by engineers working with coding agents and merged through your CI. The backlog items that hold up enterprise deals get finished.
Internal tools for operations and support
Admin panels, support consoles and billing tools, so engineers stop running database queries for colleagues. Built on your data with proper roles and audit logs.
Data pipelines and product analytics
Event data from the product, Stripe and your CRM lands in a warehouse with tested models. Revenue, usage and churn figures agree across finance, product and sales.
AI roadmap for the product
A short assessment of where AI adds value to your product and operations, ranked by impact and effort, with the data and architecture each option needs. Your board gets a plan grounded in your codebase and customers.
Built around the rules.
What we design for from the first week. Your legal and compliance people keep the final word.
SOC 2 and ISO 27001 evidence
Enterprise customers and later-stage investors ask for SOC 2 or ISO 27001 evidence. We work within your access policies, use least-privilege accounts and leave audit trails that support your certification.
IP assignment and open source licences
Due diligence checks that the code belongs to the company and that dependencies are licensed compatibly. Our contracts assign IP to you, and we track the licences of dependencies we add.
GDPR in product and data work
Analytics pipelines and support tools touch personal data. We apply pseudonymisation in the warehouse, restricted access in admin tools and a processing agreement for our own access.
Works with what you run.
If a system has an API, a database, an export or an inbox, we can build on it. These are the ones we meet most.
- GitHub
- GitHub Actions
- Linear
- Jira
- AWS
- Google Cloud
- Kubernetes
- Postgres
- Sentry
- Datadog
Where to start.
A bounded first stream
We start with one well-defined piece of work, such as an SSO and SCIM integration or a new reporting module, with clear acceptance criteria. It shows how we fit into your process before you hand us a larger stream.
Talk it throughWhat it includes
- Onboarding into repositories, CI and rituals
- Design review with your tech lead
- Delivery through your pull request process
- Handover notes and runbook
Related work.
A guide to start with.
Further reading.
Agentic engineering: how to ship SaaS faster with coding agents
How senior engineers use coding agents to ship SaaS faster while keeping control of architecture, quality and security.
4 min read
Build vs buy software: when custom development pays off
A practical way to decide between off-the-shelf software and custom development, covering total cost of ownership, integrations and risk.
5 min read
LLM evals: how to test AI features before every release
A practical approach to LLM evals: build a test set from real cases, combine code checks with model grading, and block releases that regress.
5 min read
Common questions.
You work with a team that already delivers together, with shared review habits, agentic tooling and cover when someone is away. It sits under one agreement, and we manage the people.
Yes. We work in your Slack, GitHub, Linear or Jira and join the rituals that matter for our stream. Most teams treat us as a squad within engineering.
We plan for it. Documentation, runbooks and pairing sessions transfer the work to your new engineers, and we can stay on for a period to support them.
It goes through the same review, tests and security scans as any other code in your repository. We use agents on business accounts that do not train on your code, and the IP is assigned to you.
Yes. We review architecture, test coverage, dependency licences and security practices, then fix the gaps investors or buyers are likely to raise.