AIDATechAI Digital Assistance

AI-assisted application development

Make AI a real coworker on your dev team.

We help software teams move to spec-driven development: product managers, engineers and DevOps share one living specification, and AI coding agents build from it. We train your people, guide the migration and stay until the new way of working sticks.

Plan a pilot How it works
Product goals, acceptance criteria and operations rules feed one living spec. AI coworkers build from it, engineers review, and the DevOps pipeline verifies against it. What the team learns flows back into the spec. Product goalsproduct manager Acceptance criteriaPM + engineering Ops & security rulesDevOps Living spec one source of truth AI coworkers buildcode, tests, docs Engineers reviewdesign & quality calls Pipeline verifieschecks trace to the spec what the team learns updates the spec
Everyone writes into the same spec. People and AI agents build from it, and the pipeline checks the result against it.

AI writes code fast. Without a shared spec, it builds the wrong thing fast.

Most teams add AI assistants to the same old handoffs, so the speed gains get lost in rework. Spec-driven development moves the conversation to the front: intent, acceptance criteria and operational constraints are agreed in one place before anyone, human or AI, writes code.

Without a shared spec

  • Tickets are vague, so AI fills the gaps with guesses
  • Engineers chase clarifications and rewrite generated code
  • Reviews pile up because nobody agreed what “done” means
  • DevOps learns about new requirements at release time

With spec-driven development

  • The spec states the goal, the rules and how success is tested
  • AI agents plan and build against it; engineers direct and review
  • Acceptance criteria become tests, so “done” is checkable
  • Deployment and security needs are in the spec from day one
  1. IntentThe PM describes the problem, users and outcome.
  2. SpecPM, engineering and DevOps agree criteria and constraints.
  3. PlanAI drafts a technical plan and tasks; engineers adjust it.
  4. BuildAI coworkers write code and tests; engineers review.
  5. VerifyThe pipeline checks the change against the spec.
  6. LearnWhat changed or surprised you updates the spec.

Closer collaboration from product to production

A living spec gives every role the same picture of what’s being built and why, so the AI on the team has the context it needs too.

Product managers

Your requirements become what gets built

Write intent and acceptance criteria once, in plain language, and see agents and engineers work directly from them. Fewer “that’s not what I meant” cycles, and progress you can read without decoding code.

Engineering

More design and review, less boilerplate

Engineers set direction, make the architecture calls and review AI output against clear criteria, instead of guessing at tickets or hand-writing routine code. Context lives in the repo, not in someone’s head.

DevOps & platform

Guardrails in from the start

Deployment, security and compliance needs are part of the spec, and pipeline checks trace back to them. AI-generated changes arrive with tests and the context to approve them with confidence.

Leadership

AI adoption you can see and steer

Move from scattered individual tool use to a shared, repeatable practice, with traceability from request to release and a clear view of where AI is helping and where it isn’t yet.

How we get your team there

A practical path from first experiments to an everyday way of working, built around your real projects, repositories and tools.

Readiness assessment

We map how work moves today from idea to release, where AI is already in use, and the risks and gaps. You get a recommended pilot team, project and success measures.

Role-based training

Hands-on workshops for each role: PMs writing specs AI can act on, engineers directing and reviewing AI coworkers, and DevOps encoding guardrails and pipeline checks.

Guided pilot migration

We work alongside one team on real work, setting up spec templates, repository conventions, agent instructions and CI/CD checks tied to the spec, and adjusting as you go.

Scale and coach

We turn what worked into a playbook for more teams, track the measures you care about and keep coaching until the practice runs without us.

Tool-agnostic: we work with the AI coding assistants and agents your team prefers, alongside your existing Git, ticketing and CI/CD tools.

Beyond the development team

We still help businesses put AI to work in the systems they already run.

Add AI to existing apps

Our AIDA Accelerator connects your CRM, ERP, apps and data to any AI model provider through one integration layer.

Decision Assistance

Live sales, supply chain and customer data turned into forecasts, risk assessments and scenarios, with your experts making the final call.

New AI applications

Chat and assistant applications built fast on open-source tools and cloud platforms, without locking you into one provider.

We’ve lived the handoffs we’re helping you fix

For 30 years we’ve built software and integrated systems, working between product owners, development teams and operations. AI coworkers are the biggest change to that work we’ve seen, and the teams getting the most from them are the ones that agree on what to build before they build it.

We’re based in Chicago and work with teams at every stage, from engineers trying AI assistants for the first time to organizations rolling out AI-assisted development across many teams.

Ready to bring AI onto your development team?

Tell us about your team, your current workflow and the tools you use. We’ll suggest a pilot that fits.

Plan a pilot