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Blog / Category
Platforms & migrations
Redesigns, migrations, integrations, speed and security, without losing what already ranks or earns.
Redesigns, migrations and integrations are where businesses quietly lose years of work: addresses change, data stops flowing, pages get slower, and nobody notices until the numbers drop.
We treat every move as a release that must prove it changed nothing for the worse: every page compared before launch, every integration built to survive systems that fail and throttle, and sensitive data handled with access control and a trail.
This is for you if
- A redesign or platform move is unavoidable and your site earns too much to risk it
- Your systems do not talk to each other, or only through fragile workarounds
- You handle regulated or sensitive data and need integrations you can trust
Articles
Production: what a prototype skips and a real system can’t
A prototype assumes every system answers and every input is clean. Production plans for the day they don’t. What we build into every system so it keeps working, and keeps someone responsible for it.
Redesign without losing what ranks: compare every page before launch
A redesign is the most common way to lose traffic you already earned. How we rebuilt a site, cut phone load time to 1.9 s, and compared 3,050 pages before anyone saw it.
One system, many customers: proving their data stays apart
A product that holds several companies’ sales calls must never let one see another’s. Saying so is easy. Here is how we checked it on the live system, and what we built so the answer stays true after every change.
Normalization: same customer, five IDs
Every system has its own idea of a customer, a note or a date. Normalization turns them into one validated shape, and it quietly decides what your AI will say. Lessons from an AI platform for lawyers and clinicians.
Connecting AI to your tools safely with MCP
MCP lets one integration serve every AI assistant your team uses. How to expose your systems through it without handing a model more power than it needs: narrow tools, read-only first, approvals and a log of every call.
Integrating APIs that fail: retries, idempotency and backoff
Every external API will time out, throttle you or change without notice. The patterns that keep an integration correct when that happens: backoff with jitter, idempotency keys, retry budgets, dead-letter queues and reconciliation.
Build on your own accounts from day one
Who holds the repository, the cloud account and the AI keys decides how easy it is to change partners, hire a team or raise the next round. Why we build on the client’s own accounts from the first day.
Least privilege for AI agents and integrations
An AI agent can do exactly as much damage as its credentials allow. How to scope keys, tokens, tools and write access so your AI integrations stay useful and contained.
Your security policy may be hiding your revenue
A site’s own security settings were silently blocking its analytics and ad tracking, while one bot out-crawled Google. Two security findings from the first month of an engagement.
Limit the blast radius: decide what a failure is allowed to break
Every integration and every AI feature will fail at some point. Deciding in advance how far that failure can spread is what keeps one bad source, one bad deploy or one bad model output from taking the rest of the business with it.
Ingestion, normalization, trust: feeding practitioner data to an AI assistant
An AI assistant for legal and healthcare practitioners is only as good as the records it can read. What we learned owning the integration and data layer of such a platform, and why calling an endpoint is the easy part.
Observability for AI features: what to log, trace and watch
When an AI feature misbehaves, you need to know which request, which prompt and which model did it, and what it cost. What to log, trace and watch so you can answer that in minutes, not days.
Put one gateway between your product and the AI models
When every feature calls AI providers directly, keys, costs and personal data end up scattered across the codebase. One internal gateway gives you a single place to control them, and lets you change models without touching a feature.
Fourteen websites, one platform: lessons from consolidation
Many organisations end up running a dozen sites that drifted apart. What we have learned merging them into one platform, and shrinking the code while everything stays live.
Webhooks or polling: keeping your AI's context fresh
An AI assistant that reads yesterday’s data gives yesterday’s answers, with full confidence. How to choose between webhooks, polling or both, and how to keep the data your AI reads as fresh as the decisions it supports.
Data contracts: agree on the shape before you connect two systems
Integrations break quietly when two systems disagree about what a record looks like. A data contract makes that agreement explicit, testable and owned, so bad data stops at the boundary instead of reaching your reports and your AI.
Related work
From a traffic collapse to a measurable affiliate business, in 10 weeks
Affiliate website · client details withheld
The site had lost about 90% of its Google traffic. We proved why, cut it to what earns, rebuilt it, and tied every affiliate click to the page that earned it.
Affiliate & publishing
Case data for a legal AI assistant
Practitioner AI platform · client details withheld
Integrations with legal case-management systems for an AI assistant used by law practices: ingesting contacts, cases and notes through REST and OAuth2, and normalizing them into validated records the assistant could use as context, with retries, rate limits and traceable logs around every call.
Legal services
Patient data for a health AI assistant
Practitioner AI platform · client details withheld
Integration work with healthcare practice, health-record and wellness systems for an AI assistant used by clinicians: ingesting practices, patients, notes and test results across REST, SOAP/XML, API keys and webhooks, normalized into one validated model, and handled to HIPAA, SOC 2 and GDPR expectations.
Healthcare
Enterprise API integration in a regulated data environment
Enterprise integration · details withheld
Architecture and backend development for a large enterprise API integration built on a cloud API gateway, in a security- and compliance-sensitive data environment: data architecture, API design, access control, security and compliance requirements, worked through directly with stakeholders.
Enterprise data
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