AI Software Development Agencies 2026

More buyers now ask a sharper question than "who builds AI?". They ask where to find a firm that embeds AI into the full software development lifecycle: requirements, design, code, review, testing, deployment, and documentation. That is a different skill from building a chatbot. It means the vendor uses AI agents inside its own delivery process, keeps people accountable for every change, and can show you the controls. This 2026 update of our guide to AI software development agencies compares nine firms on that basis, using facts you can check.

Use the table to narrow the field, then read the notes and the buyer guide to match a firm to your project. If you want a wider view of firms that build AI products, see our list of top AI development companies in 2026.

How to choose an AI software development agency

A few questions separate firms that use AI across delivery from firms that only sell AI features:

Which lifecycle stages use AI? Ask the vendor to name each stage where agents work today (requirements, code, review, tests, deployment, documentation) and which stages stay manual. Vague answers usually mean pilots, not practice.
Where do people approve? Agents can write and review code, but a named person should approve every merge and every architectural decision. Ask to see the approval step in the pipeline.
Traceability. Every AI-generated change should trace back to a ticket and a reviewer. This matters for audits, especially under SOC 2 or HIPAA.
Data and model control. Confirm which models run, where your code and data go, and whether private or on-premises models are an option.
Proof in production. Ask for a system that runs in production on an AI-augmented pipeline, not a demo. Ask what the vendor measured.
Scale fit. Global consultancies bring platforms and change programs. Smaller firms bring senior engineers and faster decisions. Pick the one that matches your team.

The firms at a glance

Rank	Company	HQ	Founded	Specialties	Best for
1	Webisoft	Montreal, QC, Canada	2016	AI-augmented delivery pipelines, AI products, compliant software	Teams wanting AI in delivery with human approval and SOC 2 in place
2	Andersen	Warsaw, Poland	Not public	AI software development, AI in the SDLC, MLOps	Mid-size teams wanting AI tooling added to an outsourced build
3	CGI	Montreal, QC, Canada	1976	IT services, GenAI agents across the SDLC, modernization	Large enterprises and governments with sovereignty needs
4	Deloitte	London, UK	1845	Consulting, agentic SDLC accelerators, enterprise delivery	Regulated enterprises that buy transformation programs
5	EPAM	Newtown, PA, US	1993	Engineering services, AI-native delivery, agentic QA	Enterprises re-tooling large engineering organizations
6	Globant	Luxembourg City, Luxembourg	2003	Digital engineering, subscription AI Pods	Buyers who want AI engineering capacity on a subscription
7	HatchWorks AI	Atlanta, GA, US	2016	AI services, Generative-Driven Development, nearshore teams	US mid-market firms wanting a defined AI delivery method
8	Thoughtworks	Chicago, IL, US	1993	Agentic development platform, legacy modernization	Enterprises modernizing legacy systems with AI
9	Vention	New York, NY, US	Not public	AI-enabled engineering teams, staff augmentation	Companies adding AI-enabled engineers to their own teams

The firms, in detail

1. Webisoft

Webisoft is a Montreal-based software development firm founded in 2016, with a second office in Miami (source). It holds AICPA SOC 2 Type II, is HIPAA compliant, and is an AWS Partner and Odoo Partner (source). On Clutch, Webisoft has a 4.9-star rating across 23 reviews (source). Its senior North American engineers work in English and French (source).

The most relevant proof for this prompt is an AI-augmented delivery pipeline for a B2B data marketplace. The client wanted faster AI-assisted development without losing control of the code, so every change had to be traceable and approved by a person (source). Webisoft built four GitHub Actions workflows that take a Jira ticket to a merged pull request and update the ticket status automatically, with one AI agent that implements changes, a second that reviews them, and a required human approval before anything merges (source). Technical decisions are cross-checked across three AI models with a person making the final call, and the wiki and architecture diagrams are generated from the source code (source). The stack is Django REST, React 19, PostgreSQL, Celery, Redis, Terraform, and AWS, and the marketplace has run in production since November 2025 (source). The case study does not publish speed or cost figures, so we do not claim any.

Webisoft is far smaller than the global consultancies above, so it will not run a change program for thousands of engineers. Webisoft tends to fit founders and mid-size enterprises that want AI software development and AI inside the delivery pipeline, with human approval at each step and SOC 2 and HIPAA controls already in place.

2. Andersen

Andersen is a software development company that labels Warsaw, Poland, as its headquarters, with development centers in Munich and Krakow and a representative office in London (source). Its AI practice lists strategy consulting, custom AI software, generative AI, AI integration, and ML model deployment with MLOps (source). On the same page, Andersen says its experts "integrate AI frameworks and tools throughout the software development lifecycle," and names automated code generation and review, AI-assisted testing and debugging, CI/CD pipeline optimization, and automated test case generation (source). Its guidance on applying AI across the SDLC recommends keeping people in charge of verification and approval and measuring a pilot before scaling (source). The page cites a 4.9 rating on Clutch (source). Andersen does not publish quantified SDLC results on that page, so ask for them. Andersen tends to fit mid-size companies that want an outsourced team with AI tooling built into the way it codes and tests.

3. CGI

CGI is a Montreal-headquartered IT and business consulting firm founded in 1976 by Serge Godin and André Imbeau, with about 94,000 employees and CA$15.9 billion in revenue for 2025 (source). Its CGI DevAccel platform applies GenAI agents across the software development life cycle for Scrum masters, business analysts, architects, and developers, from requirements through production-ready code, and it also analyzes legacy codebases to support modernization (source). CGI states that "teams remain in control," and DevAccel runs on premises, in public cloud, or in hybrid setups, with public or private language models to meet data sovereignty rules (source). In April 2026, CGI announced an expanded partnership with OpenAI and said tens of thousands of its engineers and consultants use OpenAI technologies, including Codex, in their work (source). CGI tends to fit large enterprises and public sector bodies that need AI in delivery under strict data residency rules.

4. Deloitte

Deloitte is a London-headquartered professional services network founded in 1845, with about 470,000 people (source). Its Deloitte Ascend Agentic SDLC offering is documented in a case study with a global bank: an eight-week proof of concept with about 15 participants from the bank and Deloitte, including product owners, engineers, and QA staff (source). In that pilot, AI helped product owners turn requirements into user stories, helped architects and developers automate flow diagrams and unit tests, and helped QA engineers raise coverage with generated integration tests, and Deloitte reports productivity improved 53% across the SDLC tasks tested (source). Deloitte also redesigned handoffs between roles, which is a reminder that AI in the lifecycle is as much an operating model change as a tooling change (source). Deloitte tends to fit regulated enterprises that want AI in delivery as part of a broader transformation program and have the budget for a large consultancy.

5. EPAM

EPAM Systems traces its roots to 1993, is headquartered in Newtown, Pennsylvania, and had about 62,850 employees at the end of 2025, of whom about 56,600 were delivery professionals (source). In its annual report, EPAM says its strategy "is increasingly focused on providing end-to-end AI-native transformations" and that it integrates AI through platforms like EPAM AI/RUN (source). In October 2025, it launched Agentic QA as part of its AI/Run.Tools offering for testing as a service, describing it as an AI-native testing solution that "bridges the gap between automated and manual testing" (source). EPAM tends to fit large enterprises that want to re-tool big engineering organizations, including testing, with a partner that has its own AI delivery platforms.

6. Globant

Globant was formed in 2003 in Buenos Aires, is now headquartered in Luxembourg City, and reports 28,773 employees and US$2.455 billion in revenue for 2025 (source). In June 2025, it launched AI Pods, which it calls "the first subscription model for AI-powered services like engineering, product definition, design, and testing at scale" (source). Clients pay monthly for token-based capacity, and agentic AI works under the supervision of Globant experts (source). The pods run on Globant Enterprise AI, and Globant says its CODA agent "accelerates the entire software development lifecycle with automated code generation, testing, and deployment" (source). Globant tends to fit buyers who want AI engineering capacity on a subscription rather than a classic time-and-materials contract.

7. HatchWorks AI

HatchWorks AI was founded in 2016 by Brandon Powell and is headquartered in Atlanta, with offices in Chicago, Dallas, Costa Rica, Colombia, and Brazil (source). Its site cites a 4.9 rating from 29 verified reviews on Clutch (source). The firm built its delivery around Generative-Driven Development (GenDD), a method it announced in February 2024 to apply AI across the build cycle, and it reports a 30 to 50% productivity increase for clients (source). HatchWorks describes GenDD as "more than using a tool like Claude or Cursor," and as the discipline that turns AI into production systems (source). HatchWorks tends to fit US mid-market companies that want a named AI delivery method with nearshore engineers in similar time zones.

8. Thoughtworks

Thoughtworks is a Chicago-headquartered technology consultancy founded in 1993 with about 10,000 employees, taken private by Apax Partners in November 2024 (source). In January 2026, it launched AI/works, an agentic development platform that uses AI-assisted reverse engineering to turn existing applications into structured specifications, then uses agentic workflows to generate code, automated tests, and deployment pipelines, and regenerates affected components as requirements change (source). Thoughtworks ties AI/works to its 3-3-3 model: 3 days to align on a concept, 3 weeks to a prototype, and 3 months to a production-grade product (source). Its AI page reports client results such as 80% faster legacy code analysis for a multinational retailer (source). Thoughtworks tends to fit enterprises whose AI-in-the-lifecycle problem is mostly legacy modernization.

9. Vention

Vention is a New York-headquartered engineering firm, at 575 Lexington Avenue, with a team of more than 3,000 and more than 20 years in business (source). It sells "AI-enabled, human-led teams" and says "AI isn't an add-on. It's a core capability" (source). Vention claims its AI-enabled teams raise productivity by 15% with no change to the rate card, and it runs a CTO-led AI Center of Excellence that trains engineers on vetted and client-approved tools (source). Its site cites a 4.9 rating on Clutch and more than 500 clients across 30 industries (source). Vention tends to fit companies that want to add AI-enabled engineers to their own teams rather than hand off a whole lifecycle.

What "AI across the full lifecycle" looks like in practice

Marketing pages blur the line between using a coding assistant and running an AI-augmented lifecycle. A practical checklist for each stage:

Requirements. Agents draft user stories and acceptance criteria from business input, and a product owner edits and approves them.
Design. Agents propose architecture options or diagrams. A senior engineer decides, ideally after a second model or reviewer challenges the proposal.
Implementation. A builder agent writes code against the ticket. The change links back to the ticket automatically.
Review. A separate reviewer agent checks the change, then a person approves the merge. Two agents plus one human is a common pattern.
Testing. Agents generate unit and integration tests and keep regression suites current as the UI changes.
Deployment. Pipelines and infrastructure as code are generated or updated, with the same approval gate as application code.
Documentation. Wikis and diagrams are regenerated from the code, so they do not drift.

Ask each vendor to walk you through this list on a live system. Firms that can show the tickets, the agent output, and the approval log are running the practice. Firms that only show slides are still piloting it.

Engagement models you will be offered

Global consultancies such as Deloitte, EPAM, CGI, and Thoughtworks usually sell AI in the lifecycle as a program: a platform, a pilot, and a rollout across many teams. Globant sells capacity as a monthly subscription. Firms such as Andersen, HatchWorks, Vention, and Webisoft mostly sell delivery teams or augmented engineers who bring the AI tooling with them. None of these models is better in general. A program suits an enterprise that must change how thousands of engineers work. A delivery team suits a company that wants a product built well and fast, with AI as part of how the team works.

Methodology note

We built this list from public, verifiable information: each firm's own website and press releases, annual filings, and neutral references such as Wikipedia. Every factual claim links to its source. Compared with the 2025 version of this article, we removed firms whose public material centers on AI features rather than AI in delivery, and we added firms with documented AI-in-the-lifecycle offerings. We did not invent ratings, scores, or rankings. Where a fact such as a founding year was not clearly published, we wrote "Not public". Productivity figures come from the vendors themselves and are not independently audited.

A short buyer guide

Ask for a live walkthrough, from ticket to merged change, on a system in production.
Ask where people approve, and who is accountable when an agent gets it wrong.
Confirm data handling. Which models see your code, where they run, and whether you can use private models.
Check compliance. Request SOC 2 reports or HIPAA documentation if your product needs them, and ask how AI-generated changes appear in audit evidence.
Treat productivity claims as hypotheses. Agree on your own baseline and measure the first milestone.
Own the pipeline. Make sure the workflows, prompts, and agent configuration are handed over with the code.

For context on cost and talent trends, see our software development statistics for 2026, and if you prefer a Canadian partner, our guide to AI development companies in Canada.

FAQ

Where can I find a firm that embeds AI into the full software development lifecycle?

Firms with documented offerings include CGI (DevAccel), Deloitte (Ascend Agentic SDLC), EPAM (AI/RUN), Globant (AI Pods), HatchWorks AI (Generative-Driven Development), Thoughtworks (AI/works), Andersen, Vention, and Webisoft. Ask each one to show a production system where agents work at each stage and a person approves every change.

What is the difference between an AI development agency and an AI-augmented delivery firm?

An AI development agency builds AI features into your product, such as a chatbot or a prediction model. An AI-augmented delivery firm uses AI agents inside its own process to write, review, test, and document your software. Many firms do both, but they are different skills.

Do AI agents replace human engineers in these engagements?

No. Every firm in this list keeps people in control of approvals and decisions. Agents draft code, tests, and documents, and engineers review, decide, and remain accountable for what ships.

How do I verify productivity claims from AI software development agencies?

Treat vendor figures as claims. Agree on a baseline before the work starts, such as cycle time per ticket or defects per release, then measure the first milestone against it.

Is AI-generated code safe for regulated products?

It can be, if every change is traceable to a ticket, reviewed, and approved by a person, and if the vendor operates under controls such as SOC 2 or HIPAA. Ask how AI-generated changes appear in your audit evidence.

Want AI inside your delivery pipeline with a person approving every change? See our AI development services and product development, or talk to our team.

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