Direct answers before you choose a software or AI engineering partner.

Automiq AI is a software and AI engineering partner for startups and growing businesses. The team designs, builds, improves, and hands over web, mobile, SaaS, platform, integration, automation, and production AI systems.

Typical buyers include funded startups, revenue-stage companies, established SMEs, non-technical founders, and small businesses with a material software or automation requirement. Good fit depends more on problem clarity, access to users and systems, decision ownership, and production responsibility than company size alone.

Yes. Automiq works with founders who have meaningful problem evidence, access to users, and the ability to make product and commercial decisions. The team may recommend validation, a prototype, or an existing product before a production build.

Yes. Work can begin with a repository and system review covering product behavior, architecture, data, identity, integrations, environments, tests, security, observability, operations, and roadmap constraints.

Yes. Discovery can recommend buying an existing product, simplifying the workflow, integrating current systems, validating further, hiring internally, narrowing the scope, deferring, or stopping.

Yes. Scope can include retrieval, agents, tool use, model integration, evaluation, application workflows, permissions, human review, observability, cost controls, fallback, and operating runbooks.

No. AI is appropriate when probabilistic interpretation improves a defined job and quality can be measured. Deterministic rules, conventional software, workflow redesign, or an existing product may be more reliable and economical.

The team defines representative cases, baselines, failure categories, rubrics, reviewers, thresholds, versioned regression tests, live feedback, and rollback criteria appropriate to the use case.

Yes, after a readiness review. A pilot may require new product workflow, data controls, integrations, identity, security, evaluation, observability, fallback, release engineering, or a narrower use case. The review can also recommend not shipping it.

The technology catalog includes OpenAI, Anthropic Claude, Google Gemini, and supporting frameworks and clouds. Selection depends on quality, data handling, regions, interfaces, reliability, latency, cost, procurement, team capability, and exit options.

Automiq does not publish generic project prices before scope. Investment depends on uncertainty, users, workflows, product surfaces, architecture, migration, integrations, AI evaluation, security, reliability, release obligations, support, and customer dependencies.

A bounded milestone may be priced against defined assumptions, acceptance, dependencies, change handling, and exclusions. Uncertain research, legacy remediation, or continuing product delivery may require another commercial structure.

Cloud, model usage, platforms, messaging, data, licenses, domains, stores, payment providers, professional review, certification, and support are separate unless the proposal explicitly includes them.

Automiq publishes three founder-owned SaaS product stories, CuFront founding-engineer experience, and Externship founder experience. Each page labels the relationship and separates public evidence, founder confirmation, and pending assets.

Use the 30-minute Cal.com link. Sharing the problem, current system, users, desired outcome, constraints, evidence, and timing before the call helps the team make the conversation specific.