Use cases · Developers

Use cases for developers

You can build all of this yourself. The question is whether you should spend your next three sprints on review bots, webhook plumbing and an LLM feature nobody has tested on real data. These are the pieces we design and hand over with the code.

Sound familiar?

Reviewers opening a large PR with no context
Release notes written from memory on release day
Webhooks that drop events silently, with no replay
An AI feature that demos well and fails on real inputs

Code review and delivery

Less time on the paperwork around shipping, and more context when people review.

Pull request review helper

Big PRs sit for days because reviewers do not know where to start.

What we build

1A GitHub Action that runs on every pull request
2Summary of what changed and why, with the risky files called out
3Checks against your own conventions file, not generic advice
4Posted as one comment that updates on each push

GitHub ActionsClaude or GPT APIYour existing lint and tests

You stay in control: It comments, it never approves. Merges stay with your reviewers.

What changes: Reviews start with context, so they start sooner.

Changelog and release notes from merged work

Release notes get written from memory, so they are late, thin or wrong.

What we build

1Merged PRs grouped by feature, fix and internal change
2A technical changelog and a plain-language customer draft
3Posted to Slack for edits before publishing

GitHub APIClaude or GPT APISlack

You stay in control: A person edits and publishes the customer version.

What changes: Release notes are ready when the release is.

Error and incident triage

The same error fires a hundred alerts, and nobody knows who owns it.

What we build

1Errors from Sentry or your logs grouped and de-duplicated
2A summary with the stack trace, recent deploys and likely owner
3A ticket opened in Linear or Jira, and the owner pinged in Slack

SentryLinear or JiraSlackn8n or a small service

You stay in control: Severity and fixes stay with the on-call engineer.

What changes: One ticket with context instead of an alert storm.

Integrations and APIs

The plumbing between your product and the tools around it, built so it does not lose data.

Webhook handling you can trust

A provider retries, you process the payment twice. Or it times out, and the event is gone.

What we build

1Endpoints that verify signatures and acknowledge fast
2A queue with retries, idempotency keys and a dead-letter store
3A small admin view to inspect and replay failed events

FastAPI or Node.jsPostgreSQLRedis or a managed queueDocker

You stay in control: Failed events wait for a person to replay them. Nothing is dropped.

What changes: Duplicates and missing events stop being a support problem.

Sync between your app, billing and CRM

Sales sees one customer status, billing another, and your database a third.

What we build

1One source of truth decided per field
2Stripe or Razorpay events written to your database and pushed to HubSpot or Zoho
3A nightly check that reports anything out of sync

Stripe or RazorpayHubSpot or Zoho CRMPostgreSQLn8n or custom workers

You stay in control: Mismatches are reported, not silently overwritten.

What changes: Every team looks at the same customer.

AI inside your product

LLM features with the boring parts done properly: evaluation, cost limits and fallbacks.

Ship an LLM feature that holds up on real data

The prototype worked on ten hand-picked inputs. Production has ten thousand messy ones.

What we build

1Structured outputs with schemas, not free text you parse with regex
2An evaluation set built from your real data, run on every prompt change
3Cost and latency limits, caching, and fallback between providers
4Logging that shows what the model saw and returned

OpenAI APIAnthropic Claude APIVercel AI SDKLangGraph when agents are needed

You stay in control: Prompt and model changes ship only when the eval set passes.

What changes: You can change the prompt or the model without guessing what breaks.

Assistant over your docs and code

New teammates and users ask the same questions that are already answered somewhere in the docs.

What we build

1Docs, READMEs and selected repositories indexed with access rules
2Answers with links to the exact source section
3A feedback button that collects the questions it answered badly

PostgreSQL with pgvectorClaude or GPT APINext.jsGitHub

You stay in control: Low-confidence answers say so and point to a person.

What changes: Fewer repeated questions, and a list of the docs that need fixing.

Internal tools and admin panels

Support and ops ask engineers to run the same database queries every week.

What we build

1A small admin app on Next.js over your existing database
2Role-based access and an audit log of every change
3The repeated queries turned into buttons and filters

Next.jsPostgreSQLAuth with rolesVercel or your cloud

You stay in control: Destructive actions need confirmation and are logged.

What changes: Engineers stop being the help desk for data.

Where to start

Start with the integration that breaks most often

Usually it is a webhook or sync between your app, billing and CRM. Making it reliable first gives every later automation a base that does not lose data.

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Common questions

Do you work in our repository and stack?

Yes. We work in your repo, follow your conventions and open pull requests like any other contributor. If we suggest a new tool, we explain why first.

Who owns the code?

You do, from the first commit. There is no private library or licence you depend on afterwards.

Which AI providers do you use?

Whichever fits the job and your data rules: OpenAI, Anthropic Claude, Google Gemini, or open models you host yourself. We design so switching later is a config change.

Not you? See the other use cases

Use cases for business owners Use cases for students