TIAC is Certified in the Claude Partner Network. Behind that one sentence are three years of experimenting, learning, sharing knowledge and setting clear rules for how AI should be used in software that matters. This is that story.

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The news, in one sentence

TIAC is Certified in the Claude Partner Network.

Our people hold Claude certifications across every part of the company, and we are building from here. You can learn more about the program at claude.com/partners.

We could leave it at that. But for our clients, and for anyone deciding whether TIAC is the right partner for their next product, the badge matters less than what it took to earn it. A certification is a snapshot. What we want to show is the habit behind it: a company that pays attention to where technology is going, invests in its people before it asks them to deliver, and treats security as a design requirement rather than an afterthought.

TIAC at a glance

20+ years

building software for clients worldwide

250+ people

engineers, architects, QA, DevOps, analysts and delivery leads

400+

Claude certifications earned by our team

ISO 27001 & ISO 9001

information security and quality management

Twenty years of paying attention

TIAC has been building software for more than twenty years. Today we are a team of more than 250 engineers, architects, testers, designers, analysts and project leads. We work with clients across the United States, the United Kingdom and Ireland, and the DACH region. A large share of that work is in financial services: banking platforms, payments, fintech products and the systems behind them.

Two decades in this industry teach a particular kind of humility. We have seen frameworks rise and fade, architectures come into fashion and quietly leave, and plenty of “next big things” that turned out to be footnotes. The lesson is not to ignore new technology, and it is not to chase all of it. The lesson is to look closely, test honestly, and adopt what holds up under real-world pressure.

That is how we approached generative AI.

2023: curiosity, with discipline

When large language models moved from research papers to everyday tools in 2023, reactions across our industry ranged from excitement to alarm. Inside TIAC, the first reaction was curiosity. Engineers started experimenting: asking models to explain unfamiliar codebases, draft tests, review pull requests and summarise long specifications.

It quickly became clear that this was not a passing trend. It was just as clear that individual experimentation, however enthusiastic, is not a strategy. For a company that builds software for regulated industries, “everyone tries whatever they like” is not an acceptable operating model. We needed three things at once:

• Broad access, so that AI skills would not be concentrated in a small group of early adopters.

• Shared knowledge, so that what one team learned could benefit everyone.

• Clear rules, so that our clients’ data, code and trust would never be the price of progress.

Everything that followed, including the milestone we are announcing today, grew out of those three commitments.
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Giving everyone the tools

The first decision was simple to state and harder to execute: AI tools should be available to everyone at TIAC, not only to a selected pilot group.

We made that choice deliberately. When access is limited, AI becomes a specialty, something “the AI people” do. When access is universal, it becomes part of how everyone works. A business analyst using AI to structure requirements, a QA engineer using it to explore edge cases, a project manager using it to summarise a week of discussion threads: none of these are headline use cases, but together they change how a team operates.

Universal access also changed the questions people asked. Instead of “should we use AI?”, the conversation moved to “where does it help, where does it not, and how do we tell the difference?” That is a far more useful question. Answering it required the next ingredient.
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Learning out loud: TIAC Talks

TIAC Talks are our internal knowledge-sharing sessions. They are not polished conference keynotes. They are colleagues showing colleagues what they have tried, what worked, what failed and what they would do differently.

We run them whenever there is something worth sharing, and with AI that has been often. Sessions are hybrid, so people can join in the office or online. Every session is recorded, so anyone who missed it, or joins TIAC later, can catch up. Most importantly, they are hands-on. The default format is a live demo, not a slide deck. If someone says a technique saves time, the room gets to watch it happen, or watch it not happen.

AI has become one of the most frequent topics. Recent sessions have covered:

• Claude Code and AI-assisted development: using an agentic coding tool on a real codebase, scoping tasks so the output stays reviewable, and recognising where human judgment remains essential.

• AI agents and the Model Context Protocol (MCP): how agents connect to tools and data sources, what that makes possible, and what new risks it introduces.

• Prompt engineering: practical techniques for consistent, high-quality output, and for writing instructions that other team members can reuse.

• AI security and responsible use: what should never be shared with an AI tool, how to think about untrusted input and prompt injection, and how our internal AI policy applies to day-to-day work.

The value of TIAC Talks is not any single session. It is the culture they create: learning is visible, it is normal to say “I tried this and it didn’t work”, and good practices spread across projects instead of staying inside one team.
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400+ certifications: how a whole company levelled up

Sharing sessions build curiosity and a common language. Structured learning builds depth. The Claude Partner Network gave us a clear framework for that second part.

Our people have now earned more than 400 Claude certifications, and the majority of our team has taken part. The number matters to us less as a statistic than for what it says about who took part. Certification was not limited to developers. It spans every function at TIAC:

• Developers, who use Claude Code and AI-assisted workflows in daily delivery.

• Architects and tech leads, who decide where AI belongs in a system and where it does not.

• QA and DevOps engineers, who test AI-assisted output and the pipelines it flows through.

• Project managers, business analysts and management, who need to understand what AI changes about planning, estimation, risk and client communication.

The learning path combined the courses in the Anthropic Partner Academy with advanced, architect-level training available to us through the partner program. The Academy gave everyone a shared foundation. The architect training went deeper into designing and delivering Claude-based solutions for production environments.

We are proud of this because nobody was pushed into it. People completed certifications alongside full project workloads because they saw the value for their own work and their own careers. For us, that is the clearest sign that AI adoption at TIAC is not a top-down initiative. It is something our people own.

For clients, the practical consequence is simple. Whoever you work with at TIAC, from the analyst in the discovery workshop to the engineer reviewing the final pull request, shares the same baseline understanding of what AI can and cannot do, and how to use it responsibly.
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Security first: AI under control

Our clients’ trust has been the foundation of TIAC for more than twenty years. Many of them operate in financial services, where data protection, auditability and regulatory compliance are not optional. AI does not change those obligations. If anything, it raises the bar.

That is why our approach to AI sits on an established security and quality foundation. TIAC is certified to ISO/IEC 27001 for information security management and ISO 9001 for quality management. These standards define how we handle data, manage access, assess risk and document our processes, and we apply them to AI-assisted work as well, rather than treating AI as a special case outside them.

In practice, three principles govern how AI is used at TIAC:

1. An internal AI policy that everyone follows. Every employee knows which tools are approved, what information may be used with them and what may not. Confidential client data, credentials and other sensitive information are covered by clear rules, and responsible use is a recurring TIAC Talks topic.

2. Human review of all AI-generated code. AI can draft, suggest and accelerate. It does not approve. Every line of AI-assisted code goes through the same review, testing and quality gates as code written by hand, and responsibility for what ships stays with our engineers.

3. Client consent comes first. We use AI on a client project only when the client has agreed to it. Some clients welcome AI-accelerated delivery from day one. Others need time, internal approvals or specific constraints. Either way, the decision is theirs, and we keep it transparent.

Why so much emphasis? Because the environment our clients operate in demands it:

• Financial regulation. Banks and fintech companies work under frameworks such as DORA, PSD2 and PCI DSS, which set strict expectations for operational resilience, third-party risk and data protection. Every tool in the delivery chain, AI included, has to fit within those expectations.

• The EU AI Act. Europe’s AI regulation introduces new obligations around transparency, risk management and governance of AI systems. Clients building or using AI in the EU, or serving EU customers, need partners who understand those obligations and build with them in mind.

• Sensitive data. Financial and personal data deserve the highest level of care. Our default assumption is that sensitive data stays protected, and AI workflows are designed around that assumption, not the other way around.

Our teams also continue to work on AI-specific security topics, such as securing AI agents and the tools they connect to, and handling untrusted input safely. We will share more of that work in future posts.
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Where AI fits in our delivery process

AI at TIAC is not a separate product line bolted onto our services. It is woven into the way we already deliver software, with people accountable at every step. Across a typical engagement, it supports the work like this:

• Discovery and analysis: structuring requirements, summarising existing documentation and preparing clearer questions for stakeholders.

• Understanding existing systems: exploring and documenting legacy or unfamiliar codebases faster, which is especially valuable in long-lived financial platforms.

• Development: using Claude Code for scaffolding, refactoring and first drafts that engineers then refine, review and own.

• Testing: extending test coverage and exploring edge cases, with QA engineers deciding what is meaningful and what is noise.

• Delivery and communication: keeping documentation, release notes and status updates accurate and up to date.

At every stage, the same rule applies: AI accelerates the work, and people make the decisions.
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What we learned along the way

Three years of hands-on work with AI have taught us a few things we now consider fundamental:

• Access without guidance creates risk. Guidance without access creates nothing. Giving everyone tools only works when it comes with clear rules, and rules only matter when people actually use the tools they cover.

• Review skills matter more, not less. The faster code can be produced, the more valuable it becomes to read it critically. We invest as much in reviewing AI-assisted output as in producing it.

• AI is not only for engineers. Some of the most useful applications we have seen came from analysts, testers and project leads, which is why certification at TIAC spans every role.

• Failures are worth sharing. The TIAC Talks that taught us the most were often the ones where something did not work. Knowing the limits of a tool is part of using it well.

None of these lessons came from a slide deck. They came from real projects and from colleagues willing to share honestly what they experienced.
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From practice to platforms: CAIT.tech and CAIT.biz

Everything we learned from internal adoption, TIAC Talks and real client work has gone into something more durable than know-how: our own AI platforms, developed by TIAC R&D. They package the same principles described above, broad access, shared knowledge and clear rules, into products our clients can use.
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CAIT.tech: governed AI across the software lifecycle

CAIT.tech brings AI into every stage of software delivery while keeping people firmly in control. An ecosystem of AI agents supports planning, implementation, code review and QA, orchestrated across the tools teams already use, such as Jira, GitHub and the IDE.

• 100% human in the loop: every change goes through mandatory code review approval, supported by automated quality scoring.

• Regulatory by design: built to support the EU AI Act and the NIST AI Risk Management Framework from day one, not bolted on later.

• Controlled change: version pinning for agent upgrades, so teams decide when behaviour changes.

• Organisational learning: a searchable skills library that captures proven prompts and practices and adapts to each team’s standards.

Adoption starts small, with a pilot in a single team on an existing repository, and grows from there. Learn more at CAIT.tech.
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CAIT.biz: company-wide GenAI in your own cloud tenant

Not every AI need is about writing code. CAIT.biz gives the whole organisation, from operations to legal to marketing, a secure way to use generative AI, deployed inside the client’s own cloud tenant so that data stays under their control.

• Ready-to-use modules: a general assistant with chat history, a document analyzer with source citations, translation into 100+ languages, AI web search, a PII redactor, text-to-speech and an assistant builder with an internal marketplace.

• Enterprise security: single sign-on through Microsoft Entra ID or OIDC, optional IP-range restrictions, no model training on user data and audit-ready logging of prompts and usage.

• Vendor independence: orchestration across leading model providers, including Claude, with the freedom to switch models as needs change.

• Structured rollout: a phased path from environment setup and alpha testing to business-user validation and go-live, typically within four to seven weeks.

Learn more at CAIT.biz.

Together, the two platforms reflect how we think about AI: CAIT.tech for the teams who build software, CAIT.biz for everyone else in the organisation, and the same commitment to governance, security and human oversight in both.
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What this means for our clients

For the companies we work with, this journey adds up to one core capability: AI-accelerated software development.

We use Claude Code in our day-to-day engineering work and on client AI projects. AI helps our teams move faster through the parts of software delivery that are time-consuming but well understood. That frees experienced people to spend more of their time on what clients actually hire them for: architecture, domain understanding, difficult trade-offs and quality.

What does not change is accountability. Our clients do not get “AI-written software”. They get software built by experienced engineers who use the best available tools, under the same review, testing and security standards as always.

When you work with TIAC, you can expect:

• A team where AI literacy is the norm, not the exception, across engineering, QA, delivery and management.

• Transparency about where and how AI is used on your project, and the ability to set the rules.

• Security and quality processes, anchored in ISO 27001 and ISO 9001, that apply to AI-assisted work exactly as they apply to everything else.

• Hands-on experience with the regulatory realities of financial services in the US, the UK and Ireland, and the DACH region.
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What certification means, and what it doesn’t

We want to be clear and modest about what today’s announcement represents.

Being Certified in the Claude Partner Network recognises that our people have invested seriously in learning to work with Claude. It shows that we have built a foundation. It is not a claim that we have everything figured out, and it is certainly not the finish line.

AI tools are evolving month by month. Some of what counted as best practice a year ago is already outdated. The only lasting advantage is the ability to keep learning, and that is the capability we have worked hardest to build.
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What comes next

We are building from here. Over the coming months, our focus is on three things:

• More certification and training, deepening our expertise, not only broadening it, with more architects and senior engineers taking advanced training.

• New AI services, building on our experience with AI-accelerated development to support clients across more of their AI journey.

• Even stronger expertise across the team, through more TIAC Talks, more hands-on experimentation and continuous sharing of what we learn on real projects.
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Thank you

Milestones like this are built by people. Thank you to every colleague who spent an evening on a course, presented at a TIAC Talk, challenged a weak prompt, reviewed AI-generated code with a critical eye or asked a hard question about data security. This certification belongs to you.

And thank you to our clients, who trust us with their products, their data and their customers. Everything described here exists to deserve that trust.
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Let’s talk

If you are exploring how AI-accelerated development could work for your product, while keeping security, compliance and quality where they need to be, we would be glad to share what we have learned. Get in touch with the TIAC team.

To learn more about the Claude Partner Network, visit claude.com/partners.

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