Software

AI for Software & SaaS Companies

Custom AI tools for SaaS startups, indie developers, dev agencies, IT consultancies, and internal tooling teams. Reclaim engineering hours from support, docs, onboarding, and ops, without buying another generic platform that almost fits.

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Where teams are stuck

The work that keeps engineers off the roadmap

Most software teams have the smarts to build anything. The problem is bandwidth. Support escalations, doc drift, onboarding hand-holding, and internal tooling consume the hours your roadmap is supposed to get.

Support tickets eat senior time

The same questions come in week after week. The answer lives in the docs, but customers don't read docs. Engineers context-switch out of feature work to handle them.

Docs that go stale the day they ship

Documentation is everyone's job and no one's job. Versions drift, examples break, and the help center becomes a graveyard nobody trusts.

Onboarding by hand

Each new customer gets a kickoff call, a setup walkthrough, a Loom or two, and a Slack DM thread. It works, and it doesn't scale, and it's the first thing that breaks at 50 customers.

Internal ops eating dev cycles

"Quick" Slack asks for environment info, deploy status, billing lookups, account changes. Each one is two minutes. Together they're hours per engineer per week.

What we build

Custom AI tools for software teams

Built to plug into your existing repo, your support stack, and your data, not to replace them. We work in your stack, your conventions, and your standards.

Support copilot grounded in your docs and tickets

Trained on your help center, your past tickets, and your code's actual behavior. Drafts responses, escalates the cases that need a human, and learns from every resolution. Cuts ticket volume to engineers without offshoring quality.

Documentation generator and freshness monitor

Reads your code and surfaces undocumented behavior. Drafts updates when APIs change. Flags doc pages that have drifted from the codebase. Keeps the help center actually correct.

Onboarding agent

Walks each new customer through setup at their pace, in-app or in a chat thread. Answers their questions in real time, shows them where to click, and only loops in your team for the genuinely hard cases.

Internal devops Slack bot

Handles the routine asks: deploy status, environment health, customer lookups, feature flag toggles. Logs every action. Cuts the death-by-a-thousand-pings that kills focus time.

Bug triage and reproduction assistant

Reads inbound bug reports, asks clarifying questions, attempts to reproduce, and tags by severity, area, and customer impact. Engineers see triaged, repro-confirmed bugs ready to fix.

Feature usage and feedback synthesizer

Pulls product analytics, support tickets, NPS comments, and sales call notes. Surfaces what users actually want next, ranked by signal strength and revenue at stake. Replaces gut-call roadmap meetings.

Sales engineer copilot

Drafts answers to security questionnaires, RFP responses, and prospect-specific technical questions, pulled from your real architecture and policies. Frees senior SEs from copying-and-pasting old responses.

Code review assistant

Catches the routine review feedback (naming, missing tests, security smells) before a human reviewer sees the PR. Senior reviewers spend their time on architecture and design, not nitpicks.

Why us

We build software too

We're not a marketing-led shop that learned to use AI APIs. We're builders who happen to specialize in AI.

Our team has shipped production software, run small companies, and sat on the boards of others. We understand the difference between a demo that wows and a system that survives a 3 a.m. PagerDuty page. We know what's worth abstracting, what's worth keeping rigid, and where AI helps versus where it just adds latency and risk.

That perspective shows up in every engagement: tools designed to integrate with your existing systems, not replace them, and built to standards your engineering team would have written themselves with more time.

Common questions

What software teams ask

Specific to the questions we hear most often. For general questions, see our main FAQ.

Why hire you instead of having our own engineers build this?

Sometimes you shouldn't. If your team has the AI experience and the bandwidth, build it in-house. We add value when your engineers' time is better spent on your actual product, when you need to ship fast without diverting a senior IC for two months, or when nobody on the team has yet shipped production AI infrastructure (model selection, prompt evals, observability, cost controls). We frequently work alongside in-house teams, not as a replacement.

How do you handle our data, customer data, and source code?

We use enterprise AI providers (Anthropic, OpenAI) under their no-training data policies, and where appropriate we deploy to your own cloud accounts so data never leaves your perimeter. Source code access is least-privilege and time-bounded. We can sign NDAs, BAAs, and DPAs, and align with your existing SOC 2, ISO, or GDPR posture.

What's your stack and how do you integrate?

We work in your stack, not ours. Python, TypeScript/Node, Go, Ruby, and the major AWS / GCP / Cloudflare deployment patterns are all in our wheelhouse. We integrate with the systems you already run, including GitHub, Linear, Jira, Slack, Intercom, Zendesk, Stripe, Segment, the major data warehouses, and the major observability stacks. Discovery includes an architecture review so the build slots in cleanly.

We're a small team. Should we wait until we're bigger?

Usually no. Small teams capture more upside per dollar because deploying a tool across a 5-person engineering org is a one-week rollout. We've also found that the discipline of building one well-scoped AI tool early sets useful patterns (evals, observability, cost monitoring) before you have ten of them sprawling. Worth doing the first one with someone who's done it before.

See what we'd build for your team.

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