AI into what you already run
You don't demolish a working building to add one service to it.
We add AI features to the software you already run: connected through your product's existing APIs and data models, designed to degrade gracefully when a model is slow or down, and fitted to your current UI rather than bolted on as a separate thing.
Survey
yourproduct.app
Reused exactly as it stands
Read and write, no schema change
Tapped into Contacts API + Auth
Read only
Tapped into Events stream
Untouched
If the new work fails, every existing service on this list still carries the product. That is the whole design.
Through your existing APIs
Fitted to your current UI
Degrades gracefully on failure
The brief
AI features that fit into the product you already have
You don't need a new platform to add AI — you need it woven into the one you run. We work through your existing APIs and data models to add features like summarization, drafting, or smart search, fit them to your current interface, and build fallbacks so a model outage degrades a feature instead of breaking the product.
AI features added to your current SaaS, CRM, or internal tool through its existing APIs — no rebuild, with fallbacks so the product still works when the model doesn't.
The tenants stay in. The work goes in around them.
What the works are for
AI in the product, not beside it
Features are fitted to your existing interface and data, so they feel like part of the product your users already know rather than a separate tool.
No rebuild to get there
We integrate through your current APIs and data models, adding capability on top of what you run instead of arguing for a from-scratch replacement.
It still works when the model doesn't
Each AI feature has a defined fallback, so a slow or unavailable model degrades that feature gracefully rather than taking the whole product down.
Schedule of works
Four packages, and none of them start with a demolition
The survey
We map the APIs, data models, and UI you already run to find where a feature attaches cleanly, and just as usefully, where it doesn't.
- API surface review
- Data model mapping
- Attachment points
The tap
The feature is built against your existing endpoints, reading and writing through the systems already in place instead of a parallel set of its own.
- API-based wiring
- Existing-auth reuse
- In-product UI fit
The isolation
Fallbacks, timeouts, and error handling, so a feature that fails takes only itself down. The rest of the building keeps working.
- Fallback behavior
- Timeout handling
- Failure isolation
The commissioning
Feature flags, a staged rollout, and usage guardrails, so new work goes live gradually and comes back out fast if it needs to.
- Feature flags
- Staged rollout
- Usage limits
The programme
Four weeks, and the product is open for every one of them
Week 1
Survey the structure
APIs, auth, and the exact place in the interface the feature has to live get mapped before anything is cut into.
Week 2
Draw the failure first
What the feature does when the model is slow or down is designed alongside the happy path, never bolted on after.
Week 3
Build behind the hoarding
The work ships gated. Users see nothing until it's ready to switch on for a slice of accounts.
Week 4
Commission a floor at a time
A small cohort gets it first. Usage and fallback behavior are watched before the flag opens any wider.
The method
Each step hangs off the one before it
Assess the surface
Review your product's APIs, data, and UI to find where AI can attach without a rebuild.
Design the fit
Shape the feature so it reads and writes through your existing systems and lives in your current interface.
Build with fallbacks
Wire the integration and its degradation behavior so a model failure never breaks the core product.
Roll out safely
Ship behind feature flags in stages, with guardrails and a fast path to pull it back.
Detail drawings
Three pieces that make one feature
Integration points
The feature is mapped against the APIs, authentication, and exact UI location it attaches to, so it's built into what you run instead of beside it.
- —Existing API surface
- —Current auth & permissions
- —UI attach point
Fallback design
What happens when the model is slow or unavailable is designed with the happy path, so a degraded state is defined behavior, never a discovery made during an outage.
- —Defined fallback state
- —Cached / last-known results
- —Clear degraded-mode UI
Rollout & flags
The feature ships behind a flag and reaches a slice of accounts first, with a fast, already-rehearsed path back if it needs pulling.
- —Feature flags
- —Cohort-based rollout
- —Fast rollback path
What the tenants notice
What actually changes once the work is in
Nobody had to move out
The feature lands inside the product your team already runs. There is no parallel platform to stand up first and no second system to maintain forever after.
A slow model is not an outage
Degraded means degraded. One feature shows a cached result or a manual path while everything around it carries on as normal.
No new keys to cut
The work reuses the authentication and permissions your team already administers, so access control stays in one place.
Users have nothing to learn
The feature appears where people already look, in the patterns they already know, instead of announcing itself as a graft.
It proves itself on a small floor
A staged cohort demonstrates the feature works on real usage before the whole tenancy ever sees it.
You can run it without us
The endpoints, the data flow, and the rollout setup are documented, so this never becomes a black box only we can open.
Selected work
Products we worked inside, never around
The handover file
What lands in your product
The feature, plus the drawings of how it attaches and the plan for reversing it, so the work stays yours after we're off site.
Integrated AI feature
The AI capability built into your existing product through its own APIs and interface.
Integration & fallback spec
Documentation of the endpoints used, the data flow, and the degradation behavior on failure.
Rollout plan
The feature-flag, staging, and guardrail setup for shipping and, if needed, reversing the feature.
Where we have done this
Every occupied building is occupied differently
SaaS platforms
A capability added inside the product your customers already pay for, through the API surface it already exposes.
CRM & sales tools
Summaries and scoring on the same record a rep already has open, reading through the contact endpoints you already run.
Internal tools
Drafting and search fitted into the admin systems your own team lives in, reusing the permissions already granted there.
Logistics & operations platforms
Signals and suggestions threaded into dispatch and tracking screens without touching the core transactional path.
The first visit
Have a product with a feature-shaped gap in it?
Tell us the product and the feature. We'll survey how it attaches through the APIs you already have.
Choosing Flaidex for this
Four rules we work to on every retrofit
We integrate through what exists
The endpoints, the auth, the data models. If a feature can attach to what you already run, that's where it attaches, instead of onto a platform we ask you to stand up first.
The failure is drawn before the feature
You see how the work degrades before it ships, instead of meeting that behavior for the first time during an incident.
Nothing lands everywhere at once
Flags and cohorts mean new work reaches a small population first, with a rollback path that has already been rehearsed.
We say when it isn't worth it
If a foundation genuinely can't carry the feature, a rebuild is the honest answer and we give it plainly. It's the exception, and we never lead with it.
Working with us
What the engagement itself is like
One team, survey to handover
The people who assess your product are the people who build the feature and hand it over. No gap opens between the strategy and the code.
Fixed scope, defined outcome
A named feature with a named result, instead of an open-ended retainer that quietly never finishes.
Your stack, your conventions
We fit the work to how your codebase already does things instead of importing a parallel system. That's what keeps it maintainable after we leave.
Questions
What teams ask before they integrate
Q1Do we need to rebuild our product to add AI?
Almost never. Most products already expose the APIs and data models an AI feature needs to read from and write to, so we integrate through those rather than replacing what you have. We assess your existing surface, find the clean attachment points, and add the capability on top. A rebuild is occasionally the honest answer if the foundation genuinely can't support it, but that's the exception, and we'll say so plainly if it's the case.
Q2What happens to our product if the AI model goes down?
The feature degrades; the product doesn't. We isolate each AI feature behind fallbacks and timeouts so that if a model is slow or unavailable, that one feature shows a graceful fallback — a cached result, a manual option, or a clear message — while the rest of the product keeps working normally. AI is treated as an enhancement that can fail independently, not a dependency that can take everything down with it.
Q3Will the AI feature feel like part of our product?
That's the goal of fitting it to your existing UI rather than bolting on a separate widget. We design the feature to live inside the interface your users already know, using your patterns and components, so it reads as a natural addition rather than an obvious graft. The integration extends the product you have instead of sitting awkwardly beside it.
Q4Can you work with our existing tech stack and auth?
Yes — that's the premise of integration work. We build against your current endpoints and reuse your existing authentication and permissions, so the AI feature respects the access controls already in place. We fit our work to your stack's conventions rather than importing a parallel system, which keeps the integration maintainable by your team afterward.
Q5How do you roll out a new AI feature without risking the product?
Behind feature flags, in stages. We ship the feature to a limited slice first, watch how it behaves on real usage, and expand gradually — with a fast path to switch it off if something looks wrong. Usage guardrails cap how much a feature can do, and the staged rollout means a problem surfaces on a small population before it ever reaches everyone.
Elsewhere in AI Integration & Development
Want AI in the product you already run?
Tell us the product and the feature you have in mind — we'll assess how it attaches through your existing APIs.
