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AI-Powered

Software Engineering

We are a software engineering team that builds with AI. With us, you ship faster than your competitors, capture the market earlier, and build a product easy to maintain.

Book a call See how we work
Senior+

engineers fluent in AI on
every engagement

3×

output per engineer vs a traditional outsourcing team

Ship first

reach the market before your competitors do

Visibility

into delivery progress, blockers, and AI ROI, via Beezi 

Built with
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What is AI-Powered Software Engineering

A software engineering team that builds with AI — not alongside it.

Most outsourcing teams write code the way they always have, then occasionally ask Copilot for a suggestion. That’s not what we do. AI-powered software engineering means every engineer on our team uses AI models — primarily Claude, Cursor, and GitHub Copilot — as active participants in writing, reviewing, and testing code throughout the project.

Most outsourcing teams write code the way they always have, then occasionally ask Copilot for a suggestion. That’s not what we do. AI-powered software engineering means every engineer on our team uses AI models — primarily Claude, Cursor, and GitHub Copilot — as active participants in writing, reviewing, and testing code throughout the project. On top of that, we run Beezi as the SDLC orchestration layer: shared context between our team and yours, smart ticket routing, real-time delivery observability, and AI usage analytics — so you always know what’s shipping, what’s stuck, and where engineering effort is going.

The result is a team that produces more output per engineer than a traditional development shop: faster feature delivery, automated test coverage, AI-assisted code review before anything reaches your codebase, and documentation generated alongside the code rather than left as a backlog item.

From your side, it looks like a well-run outsourced engineering team — sprint planning, regular demos, code in your repository, PRs you can review. The difference is in how fast it moves and how much ends up in production without defects.

Benefits What you get when you hire an AI-powered

engineering team
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More output for the same 
budget

Because every engineer on our team uses AI across the full development cycle — code generation, review, testing, documentation — you get roughly 3× the feature output per engineer-sprint compared to a traditional outsourcing team at the same day rate. You’re paying for engineering time; we make that time go further.

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Code that’s reviewed before you see it

Every PR we raise goes through an AI review pass before a human senior engineer signs off — checking architecture conformance, security patterns, performance issues, and test coverage. By the time code reaches your repository, it has been reviewed twice. Your team reviews a pre-vetted, annotated diff, not a raw changeset.

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Test coverage that actually ships with features

Most outsourcing teams write tests slowly, late, or not at all. We generate unit and integration tests alongside every feature — in the same PR, against the same spec. When we hand over a feature, it comes with coverage. You’re not inheriting a test debt you have to pay down later.

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Documentation you don’t have to chase us for

On most outsourced projects, documentation is a handover problem — the team delivers code, docs follow weeks later if at all. We generate API docs, architecture overviews, and code comments as part of the development process. Documentation is current because it’s produced automatically alongside every merge.

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Engineers who are fluent in AI, not learning it on your project

Every engineer on our team uses AI tooling — Claude, Cursor, GitHub Copilot — as a daily part of how they work, not as an occasional experiment. This isn’t something we’re piloting: it’s how we’ve been building since we founded the company. You get the output of experienced engineers who have already figured out where AI accelerates work and where it needs a human hand.

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Beezi — visibility across the entire delivery flow

On our engagements we run Beezi as the operational layer above the code: it gives shared context across engineers and your stakeholders, tracks what’s actually shipping versus what’s stuck, and shows where AI spend is going. You get line-of-sight into our work without needing to parse commits — and we get fewer mid-sprint misalignments because requirements are clear before development starts.

How We Develop

AI in our workflow, at every stage
of development

AI-Assisted Code Generation

Our engineers write production code using Claude Code, Cursor, Beezi, and GitHub Copilot — configured against your codebase conventions and architecture patterns. The result is first-pass code that already fits your stack, rather than generic output that needs rewriting to match your style.

Codebase-aware development

We configure our AI tools against your repo structure, naming conventions, and architecture decisions before writing a single line — so output matches your codebase from day one.

Feature scaffolding from spec

We use agentic tools to scaffold full features from ticket descriptions — components, services, data models, and initial tests — so engineers spend their time on the logic that matters, not the boilerplate.

Repetitive task elimination

API client generation, type definitions from schemas, migration scripts from model diffs — we automate the parts of development that slow teams down without adding engineering value.

LLM-powered product features

We build AI-powered features directly into your product — semantic search, document Q&A, chat interfaces, summarisation — using LangChain, LlamaIndex, or the Anthropic SDK.

Typical outcomes

35–55%

reduction in time to write first-pass code for new features

~80%

of boilerplate code fully automated

2–4 wks

to ship an LLM-powered product feature from scratch

AI-Assisted Code Review

Every PR we raise goes through an AI review pass before a senior engineer signs off. The AI flags architecture drift, security issues, performance anti-patterns, and coverage gaps. By the time code reaches your repository it has been reviewed twice — with inline comments explaining every decision.

Architecture conformance

We train our review tools on your architecture decisions and existing patterns — so every PR is checked against how your system is actually supposed to be built, not just whether it compiles.

Security pattern checking

Injection vectors, insecure deserialization, hardcoded credentials, missing auth checks, OWASP Top 10 patterns — caught before anything reaches your codebase.

Performance and complexity flags

N+1 queries, unnecessary complexity, memory leaks, expensive renders — surfaced with concrete fixes, not just warnings.

PR plain-English summaries

Every PR includes an AI-generated summary of what changed, why, and what to pay attention to — so your team can review our work quickly without reconstructing context from scratch.

Typical outcomes

−60%

PR review cycle time

−40%

post-deploy defect rate

3× faster

onboarding for new engineers reviewing unfamiliar code

Test Generation

We write tests alongside the code, not after it. Every feature we deliver comes with unit and integration test coverage generated in the same PR — so you're not inheriting test debt that someone else has to pay down later.

Unit test generation from source

AI analyses changed functions and classes, infers expected behaviour from docstrings and types, and generates unit tests covering normal paths, boundary conditions, and error cases.

Integration test generation from specs

Given an API spec or requirements document, AI generates integration test suites that validate contract conformance, error handling, and data flow across service boundaries.

Coverage gap detection and filling

On every PR, AI identifies untested code paths in the diff and generates tests specifically for the gaps — so coverage never regresses.

Test suite maintenance Assisted

When production code changes, AI detects which tests are now stale or incorrect and proposes updates — reducing the "tests are green but wrong" problem.

Typical outcomes

−55%

time spent writing tests manually per sprint

+35pp

average code coverage improvement within 90 days

75–90%

of AI-generated tests pass review with 
no edits needed

Documentation Delivered with the Code

We don't leave documentation as a handover promise. API references, architecture overviews, and onboarding guides are generated alongside the code — as part of the same development cycle — so what we hand over is documented, not just working.

API reference generation

AI reads your OpenAPI specs, code comments, and test files to generate complete, accurate API documentation — including example requests, response schemas, and error descriptions.

Architecture documentation

AI analyses your codebase structure, dependency graph, and ADRs to generate and maintain up-to-date architecture overviews, data flow diagrams (as Mermaid), and component relationship docs.

Runbook and playbook generation

AI creates incident runbooks from your observability alerts and past incident reports, and operations playbooks from your deployment procedures.

Codebase onboarding guide

AI generates a personalised codebase tour for new engineers: key modules, how to run the stack locally, common patterns to follow, and gotchas to avoid — reducing onboarding from weeks to days.

CI/CD pipeline setup

We configure your CI/CD pipeline as part of the engagement — GitHub Actions or GitLab CI, with test runs, build checks, and deployment steps set up by hand and handed over documented. Nothing automated that we can't explain.

Typical outcomes

−90%

time to write initial API or architecture documentation

Always

docs reflect the current state of the codebase

−40%

time to productive for new engineering hires

How it works

How an engagement with us runs

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Step 1
Scoping Call

We talk through what you need built, your existing stack, and your constraints. 
Week 1
Step 2
Proposal
We send a written proposal: scope, team composition, timeline, and pricing.
Week 1–2
Step 3
Kickoff & Technical Setup
We access your repository, configure our AI tools, and set up Beezi as the operational layer
Week 2–3
Step 4
First Delivery Sprint
We identify and prioritize the highest-ROI automation opportunities.
Weeks 3–5
Step 5
Ongoing Sprints
Regular sprint demos, PRs open to your team's review, weekly async updates. 
Ongoing
Step 6
Handover
Your team inherits a codebase with current documentation, solid test coverage, and AI-powered CI/CD.
Final sprint

The tools our engineers work with every day

Our team has settled on a specific set of AI tools through real project experience — not because they’re the most-marketed, but because they produce the best output on production codebases. We configure each tool against your codebase before starting, so they generate code and reviews that fit your stack, not generic output.

SDLC orchestration layer: shared context, smart ticket routing, delivery observability, AI usage analytics — connects our team, your stakeholders, and all AI agents in one place

In-editor code generation and agentic task execution — scaffolding, refactoring, migration tasks at speed

Code review, test generation, documentation, incident summarisation — as pipeline agents, not just chat

Real-time in-editor completion for boilerplate, repetitive patterns, and standard library usage

Honest Assessment

Where AI doesn’t belong in software development

AI makes our team faster and more consistent — but not at everything.
Here’s where we don’t apply it, and what we do instead.

System architecture decisions

AI can document architecture and flag drift — but it cannot design the right architecture for your product and constraints. Service boundaries, data models, consistency guarantees, and technology choices require deep context and accountability that AI tools do not have.

What we do instead

Our senior engineers design architecture and use AI to document trade-offs and validate consistency — not make the call.

Highly novel or research-grade code

AI code generation works well for familiar patterns. For novel algorithms, ML research, or highly optimized low-level code, output degrades — it may look plausible but be wrong. The more unique the problem, the more you must review it.

Use with caution

We use AI as a starting point and apply deep expert review on any novel or domain-specific logic.

Final production approval

No code we write goes to production without a human sign-off — ours or yours. AI reviewers miss things. AI-generated tests don't cover every edge case. Every PR requires a human approval gate before merge, and we design our pipeline that way without exception.

What we do instead

AI handles pre-merge preparation at speed; a senior engineer approves every merge. The gate stays — the queue moves faster.

Undocumented legacy codebases

AI review and test generation require the codebase to be readable and reasonably structured. On legacy code with no documentation, inconsistent naming, and tribal knowledge baked in — AI tools produce lower-quality output. If this is your situation, we scope a documentation pass before deploying AI tooling.

Use with caution

We scope a lightweight documentation sprint first — so AI has something solid to work with from the start.

What to build — that's yours

Product decisions — what to build, for whom, in what order — require your customer knowledge and business judgment. We build what you scope. AI helps us move faster inside that scope; it doesn't define it. We need a clear brief from you before development starts, not the other way around.

What we do instead

We help you translate a brief into a technical spec — then own the execution.

Replacing engineering judgment

The teams that get the most from AI treat it as a highly capable tool that still needs direction. We use AI to eliminate the low-judgment parts of engineering — boilerplate, first-pass review, documentation — so our engineers spend their time on the work that actually requires them. AI amplifies judgment; it does not substitute for it.

Our view

AI gives our senior engineers more time for the decisions that actually matter — not fewer reasons to have them.

Results

What AI engineering looks like in practice

Full Outsourcing
B2B SaaS startup: from spec to paying customers in 10 weeks

A two-founder B2B SaaS company had a signed LOI from their first enterprise customer but no engineering team. The customer expected a working product in 3 months. They needed to ship fast without accumulating the kind of technical debt that would slow them down when they hired their own engineers.

10 wks

spec to first paying customer

84%

test coverage at handover

2 wks

for first in-house hire to become productive

Team Extension
Scale-up: shipped a 6-month roadmap in 10 weeks

A Series B fintech had a fully allocated internal team and a major feature set — a new lending module — that couldn’t wait. Hiring would take 4 months. They needed engineers who could work in their existing codebase from week one without a long onboarding cycle.

10 wks

to ship what was scoped as a 6-month roadmap item

1 week

onboarding — Beezi got our engineers up to speed fast

3×

output vs their previous outsourcing vendor

LLM Feature Build
HR tech platform: AI assistant shipped before competitor launched theirs

An HR tech company knew their main competitor was building an AI-powered candidate screening assistant. They had no LLM experience in house and couldn’t wait for a 6-month hiring cycle. They needed to ship before the competitor’s launch announcement hit the market.

8 wks

to production-ready LLM feature

3 wks

ahead of competitor's public launch

+34%

demo-to-close rate after feature launch

Recruiting
Developer tools company: built an AI-native engineering team in 6 weeks

A developer tools company wanted to build AI-powered development in house but kept hiring engineers who claimed AI fluency and delivered the same output as before. They needed a way to actually assess AI proficiency — not just take candidates at their word.

6 wks

to a fully hired AI-native engineering team

4 of 4

hires still with the company 12 months later

3×

output vs previous hires at the same seniority level

Who We Work With

Companies that need engineering done — their way

We work with startups, scale-ups, and established businesses that need engineering to move faster — across SaaS, fintech, e-commerce, healthcare tech, developer tools, and enterprise software in the US, UK, EU, Canada, and Australia. How we structure the engagement depends on what fits your situation.

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Software Engineering
01.

Full outsourcing

You have a scope to build — new product, new feature set, or platform work. We take it end to end: code, tests, docs, and handover. You review PRs and attend sprint demos; we handle the rest.

  • Fixed or time-and-materials pricing
  • Tests and docs included as standard
  • First delivery in 2 weeks
02.

Team extension (outstaffing)

Your team is in place but needs additional capacity. We place AI-fluent engineers who work directly in your repo, your sprint, and your communication channels — not as a separate vendor.

  • Monthly engagement, no minimum beyond 1 month
  • Individual engineers or efficient teams
  • Fluent in Cursor, Claude Code, Windsurf, etc.
03.

Recruiting AI-fluent engineers

Want to build this capability in house? We help you hire engineers who are genuinely AI-fluent — not those who listed "ChatGPT" on their CV. We know what to look for and how to assess it.

  • Role definition and job spec writing
  • Technical screening for AI-tool fluency
  • We've hired AI-native engineers ourselves
04.

Ongoing support

Already shipped your MVP? We keep it running and improving — bug fixes, UI and logic updates, performance optimization, integrations, and environment support. No need for a full team.

  • Fixed monthly packages from $1,500/mo
  • Bug fixes, features, refactoring, integrations
  • Staging and production environment support
05.

Consulting

Not ready to build yet? Get a second opinion. Our AI and tech experts help with scalable decisions — AI strategy, architecture, tooling, and legacy modernization — with no long-term commitment.

  • USD 150/hour, on-demand
  • AI strategy, architecture & tooling guidance
  • Legacy system modernisation with Al, etc.
Recognized by Clutch

We’re proud to be named one of Clutch’s Top 100 Fastest Growing Companies in AI Development

Trusted by clients. Powered by AI.
Check the testimonials

FAQ

Questions we get asked

Honest answers to the questions clients ask before hiring an outsourcing team — what you get, how fast, who owns the code, and what AI actually changes.

Ask us directly
  1. What is AI-powered software engineering?

    AI-powered software engineering means our engineers use AI models — Claude, Cursor, GitHub Copilot — as active participants throughout the development process: writing code, running review, generating tests, producing documentation. On top of that, we use Beezi as the SDLC orchestration layer — shared context between our engineers and your team, smart ticket routing, real-time delivery observability, and AI usage analytics. Beezi is what makes the whole thing visible: you can see what’s shipping, what’s blocked, and where engineering time is going, without needing to parse commits. The combined result is roughly 3× the feature output per engineer-sprint compared to a traditional team, with test coverage and docs included as standard.

  2. How is this different from a traditional outsourcing team?

    A traditional outsourcing team writes code, hands it over, and leaves you with a test and documentation backlog to pay down. Our team ships features with tests and docs included as a default, because we generate them alongside the code rather than afterwards. The other difference is output volume: because AI handles the parts of engineering that don’t require judgment — scaffolding, first-pass review, boilerplate, documentation — our engineers spend their time on the work that actually requires them. You get roughly 3× the feature throughput for the same budget.

  3. Who owns the code you write?

    All code we write belongs to you — IP transfers to the client upon payment. On time-and-materials engagements we invoice monthly, and ownership of everything delivered that month transfers with each invoice. If you want IP ownership from day one on a T&M engagement, we can arrange an upfront deposit — which is then deducted from the final invoice. On fixed-price projects, IP transfers upon receipt of the agreed deposit, which is deducted from the final invoice. We work in your repository from day one, so there is no code transfer at handover — it is already in your codebase. IP assignment is included as a standard clause in every engagement agreement.

  4. How does pricing work?

    We work on two pricing models: fixed-price for well-scoped projects, and time-and-materials billed monthly. Both require a deposit before work starts — the deposit is deducted from the final invoice. On T&M engagements, IP transfers to you with each monthly invoice; if you want ownership from day one, we can structure the deposit to cover that. We discuss the right model on the initial call once we understand the scope — there’s no useful number to give without knowing what needs building.

  5. What are the biggest risks with outsourcing software development — and how do you handle them?

    The three most common outsourcing failures are: (1) poor code quality that creates long-term debt, (2) lack of visibility into what’s actually being built, and (3) a messy handover that leaves your team with undocumented, untested code. We address all three directly: AI-assisted review and test generation on every PR means quality is checked continuously, not just at the end; Beezi gives real-time delivery visibility to your stakeholders; and documentation is generated alongside code so handover is a planned milestone, not a scramble.

  6. What's the difference between outsourcing and outstaffing — and which is right for me?

    Outsourcing means we own the scope end to end — you give us a brief, we build it, we deliver it. You review the output but don’t manage the day-to-day work. Outstaffing means we place engineers into your team — they work in your repo, your sprint, and your processes, and you manage them directly. Outsourcing works best when you have a clear scope and want someone to own the execution. Outstaffing works best when you have an existing team that needs extra capacity and you want direct control over how engineers spend their time. We offer both, and we’ll tell you which fits your situation on the initial call.

  7. Is AI-generated code reliable enough for production?

    AI-generated code is as reliable as the review process it goes through — and that’s exactly where we focus. No AI-generated code reaches your repository without a senior engineer reviewing it and an automated test suite validating it. We don’t use AI to skip review; we use it to make review faster and more thorough. AI catches the patterns humans miss at speed; humans catch the context AI doesn’t have. The combination produces more reliable output than either alone.

Ask us directly
Get Started

Tell us what needs building

Book a 45-minute call with one of our engineering leads. We'll talk through what you need, whether it's a good fit for how we work, and what a realistic scope and timeline looks like — no sales pitch.

  • 45-min call with an engineer, not a sales rep
  • We'll tell you honestly if the scope is a fit and where it isn't
  • Rough timeline and team composition estimate on the call
  • No commitment — we agree on scope before anything starts
5.0

We are extraordinarily grateful for their hard work, expertise, and professionalism.

Hunter Honnessy CTO, SaaS Company
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