AI workflows for software teams

Find the hours your business is losing, and get them back with AI.

LaserFocused is an AI consultancy for software teams. We audit how you ship (from bug reports to code review), quantify the time lost to busywork, and design and build the AI workflows that give your engineers their focus back.

Book a strategy call See how it works

Free 30-minute strategy call · No obligation · Results you can measure

Bug fixes · today 11.5 hrs / week

with AI · after 2.3 hrs −80%

Illustrative example · real measured numbers

How we work Security-first Vendor-neutral Measurable ROI Fixed-scope audits EU-based

The problem

Your team is busy. But how much of that is actually moving the business forward?

Most teams lose a day or more every week to repetitive work that AI can now do reliably. The first step is seeing exactly where.

Engineers buried in busywork

Your best builders spend their days triaging tickets, reviewing nits, and wiring up the same boilerplate instead of shipping.

Requests lost in the noise

Bugs and ideas pile up in Slack threads and DMs. Half never become tickets; the rest arrive without the context to act on.

Only engineers can move work

Non-technical teammates can’t push anything forward themselves, so everything queues behind the dev team.

No clear picture of the cost

You can feel the drag between “done” and “shipped”, but nobody has measured the hours it actually burns.

Development workflows

Built for how software teams actually ship.

Our focus to start: the everyday engineering work that eats your team's week. We wire AI into the stack you already use (Slack, GitHub, your tracker), so delivery runs with humans in the right seats.

Issue intake that ships itself

Non-technical teammates report bugs and requests in Slack. AI triages them into structured tickets, implements the fix on its own branch, and opens a reviewed PR, so anyone can push work through your pipeline.

Slack → ticket → PR → review → ship

Automated code review

Every pull request gets an instant review for style, bugs, security, and missing tests. Your engineers spend their attention on architecture and intent, not nits.

open PR → AI review → auto-fix → approve

Slack-driven delivery

Run the pipeline from where your team already talks. Sync issues, kick off work, and merge the green PRs with a command, with humans approving the moments that matter.

/sync → /work → /merge

Parallel, self-service work

Multiple AI sessions implement issues independently, each with its own branch and PR, auto-rebasing and resolving conflicts so work lands safely instead of queueing behind one developer.

N issues → N PRs → auto-merge when green

A preview for every change

Each AI-opened pull request spins up a live preview URL, so anyone can verify the change in a click: no branch-pulling, no local setup, no “works on my machine”.

PR → preview URL → verify → ship

Your tools, AI-callable

We wrap your internal APIs and systems as AI-callable tools (MCP), so agents can safely act inside your stack (not just suggest) with the guardrails you set.

your API → MCP tool → agent action

Trust & guardrails

Trust the code your AI ships, because it follows your rules.

Auto-reviewing PRs is table stakes. We go further: we encode your repo’s real rules, processes, and examples, then loop on static analysis, tests, and review until every check is green, no shortcuts, no lowered bars.

Your standards, encoded

We capture each repo’s conventions, processes, and examples in agent memory files, so the AI writes to your rules, not generic defaults. Vendor-neutral by design.

Repeatable skills per repo

How your team implements, reviews, and releases, packaged as skills the AI runs the same way every time.

Your real checks, wired in

Linters, static analysis, type-checks, and your test suite run on every AI change. Nothing merges that isn’t green.

Green-PR feedback loops

When a check fails, the AI reads the feedback and fixes it, looping until every check passes against your standard. Never --no-verify, never a lowered bar.

Human approval gates

The merge stays a human call. Require a sign-off as a GitHub check, and when a PR is marked ready the right people or teams are tagged in Slack with a direct link, so the last review takes minutes, not days. Wired straight to your GitHub events.

PR #128 · feat: add SSO login AI

Lint & static analysis ✓
Type-check ✓
Tests · 142 passed ✓
Your repo rules (agent memory) ↻ auto-fixed 2
AI code review ✓

Required review @alex · @qa-team · pinged in Slack

All checks green Ready for approval

Every check green · no shortcuts

Approve where your team already works: Slack, Telegram, or WhatsApp. Synced with your tracker: Linear, Jira & GitHub Issues.

How it works

Three steps from busywork to measurable time saved.

A clear, low-risk path. Start with an audit. Continue to implementation only if the numbers make sense.

Audit

We map how work actually happens.

We shadow your real processes, document each step, and measure the time and cost of the manual work, no guesswork, just a clear baseline.

Process & time mapping
Tooling & data review
Opportunity shortlist

Reimagine

We design the AI-assisted version.

For each high-impact process we design a new workflow (what AI handles, what people keep) with the projected time saved and a clear before/after.

Redesigned workflows
Projected time & ROI
Risk & security review

Implement

We build it into how you work.

Optionally, we build and embed the workflows into your tools, train your team, and measure the real results against the baseline.

Build & integrate
Team enablement
Measured outcomes

Basic → Agentic → Advanced

The same workflow at three levels of AI.

Most teams are already on Basic or Agentic AI, and still spend the week on triage, review, and shipping. Advanced AI automates the hand-offs in between. Pick a workflow to see where the hours actually go.

Basic AI Chatbot-assisted 9.1 hrs/wk

~70% of companies use AI to assist

Triage & reproduce reports from Slack 110m
Draft tickets with a chatbot’s help 60m
Implement fixes (chatbot for snippets) 210m
Manual PR review round-trips 110m
Merge, deploy & update reporters 55m

Agentic AI Coding agent, hands-on 5.7 hrs/wk

~23% of companies are scaling AI agents

Triage; write a prompt per bug 80m
Coding agent implements each fix 90m
Review every diff; run tests locally 90m
Open PRs, merge & deploy by hand 60m
Update the reporters manually 20m

Advanced AI LaserFocused workflows 1.8 hrs/wk

~1% of companies have AI this mature

AI triages Slack reports into tickets 15m
AI implements each fix on its own PR 45m
Automated review flags issues instantly 10m
Engineer approves green PRs; auto-deploy 30m
Reporters auto-notified of the fix 5m

Advanced AI runs this in about 1.8 hrs/week.

7.3 hrs

saved every week vs Basic AI

81%

less time on the task

≈380 hrs

given back per year

Already using a coding agent? Advanced AI still saves 3.9 hrs/week vs Agentic AI: the gap is the automated hand-offs, not faster typing.

Only ~1% of companies have reached Advanced AI.

Most are still hand-holding chatbots or babysitting agents one step at a time. We build the tier almost no one reaches.

Become one of the 1%

Workflow times are illustrative ranges. See the real, measured numbers from our own systems below. Adoption figures: Stanford AI Index 2026 (≈88% use AI, ≈70% gen AI; agent use in single digits per function), McKinsey State of AI (≈23% scaling agentic AI), and McKinsey (2025) (≈1% call their AI rollouts “mature”). Per Deloitte (2026), even by 2027 only ~5% expect to fully integrate agentic AI.

Services

Start with clarity. Continue with results.

Two ways to work with us. Most engagements begin with an audit. Implementation is yours to choose once you’ve seen the numbers.

AI Audit

See exactly where AI saves you time.

Best place to start.

Process & time mapping of your key workflows
Quantified baseline: hours & cost today
Prioritised AI opportunity roadmap
Projected time savings & ROI per workflow
Security & feasibility review

Start with an Audit Fixed-scope engagement

Audit + Implementation

We design it, build it, and prove the savings.

When you want results, not just a report.

Everything in the AI Audit
Workflows designed & built into your tools
Integrations with your existing stack
Team training & change support
Results measured against the baseline

Talk to our team Tailored to scope

Outcomes

What changes when the busywork is gone.

The point isn’t “using AI”. It’s the focus, speed, and capacity your team gets back when the repetitive work runs itself.

Book a strategy call

Hours back, every week

We target your highest-volume manual work first, so the time savings compound month over month.

Ship the same day, not next sprint

Changes that used to wait days for a free developer go out same-day: drafted, reviewed, and green.

Consistent, auditable output

AI workflows run the same way every time, with your people reviewing only the exceptions.

Capacity without new headcount

Take on more work without burning out your team or hiring for tasks software should own.

Proof

Built for ourselves first.

We’re a young company, no wall of client logos yet. So here’s the proof we can stand behind today: the same workflows we’d build for you, already running on our own systems. Real numbers, measured, anonymised.

Advanced AI · live in production

452

AI-built PRs merged

and counting (as of May 2026)

~ 1.8 h

median, open → merge

half merge within the hour

79 %

merged the same day

changes don’t sit waiting

files in the median PR

small, reviewable batches (+92 / −23)

Anonymised aggregates from a production B2B app we build and operate, timings and diff sizes only, never client or product detail. Open → merge is the automated review-and-merge loop: it shows changes don’t sit waiting, not that a feature takes 1.8 h to build (we keep those separate in the methodology). See the full teardown →

This very site, shipped in a day

Design system, full static build, search, SEO, AI imagery, a working contact form, custom domains and CI: seven commits from empty repo to live, in under a day.

Astro 6Cloudflare WorkersCI/CDPagefind searchSEO + llms.txtAI imageryContact form

Read the build teardown

How we measure the hours

We measure Advanced-AI time from real timestamps and estimate the Basic and Agentic baselines honestly, each clearly labelled. No invented numbers, anywhere.

Read the method

Insights

Ideas on doing more with less busywork.

All insights

28 May 2026 · AI workflows

How to add automated AI code review to your GitHub pull requests

A practical setup for automated AI code review that clears the boring issues before a human looks, plus the guardrails that keep it from lowering your bar.

Read

27 May 2026 · AI coding agents

Best AI coding agents in 2026: an honest comparison for software teams

A vendor-neutral look at the best AI coding agents in 2026: what each is genuinely best at, our pick for building real workflows, and why the tool matters less than what you build around it.

Read

23 May 2026 · AI workflows

Basic, Agentic, Advanced: the 3 levels of AI for software teams

Most software teams already use AI. The real question is which level. A field guide to Basic, Agentic, and Advanced AI, and what each one actually changes.

Read

FAQ

Questions, answered.

Is the audit free?

The first call is: a free 30-minute strategy call to see if there’s a fit. The AI Audit itself is a paid, fixed-scope engagement: a quantified baseline and prioritised roadmap you own outright. Most teams find it pays for itself in the hours it uncovers.

How do you make AI-written code trustworthy?

We encode your repo’s rules, processes, and examples in agent context files, then run your linters, static analysis, type-checks, tests, and an AI review on every change, looping until every check is green against your standard, never bypassing it. The final merge stays your decision.

Do we need to be technical to work with you?

No. We handle the technical design and build. Your team brings knowledge of how the work happens today. We translate it into AI-assisted workflows.

Is our data secure?

Security comes first. We review data handling before anything is built, prefer tooling that keeps your data private, and design workflows so sensitive steps stay under your control.

How long does an audit take?

A typical audit runs a couple of weeks end to end, depending on how many processes we assess. You leave with a clear baseline and a prioritised roadmap.

What if we only want the audit?

That’s a complete engagement on its own. You leave with a quantified roadmap you could implement yourself or with anyone. Implementation is entirely optional.

Which AI tools do you use?

We’re vendor-neutral. We choose the model and tools that fit each workflow, your budget, and your security needs, not whatever we happen to be tied to.

How do you prove the time savings?

We measure the baseline during the audit and the results after implementation against the same processes, so the savings are real numbers, not promises.

Find out how many hours you could get back.

Book a free 30-minute strategy call. We’ll pinpoint your biggest time sinks and where AI pays off first, then map the fixes in a focused AI Audit. No slides, no obligation.

Prefer email? justin@laserfocused.ee