AI Into Your Existing Product. No Rebuild.
Model features, retrieval, and agents wired into the product you already run: behind your auth, behind feature flags, with every call traced, scored, and cost-attributed.
Most teams do not need a new AI product. They need the product they already ship to do two or three things smarter. We dock an AI layer against the surfaces you already expose, ship one capability at a time behind flags, and gate every release on eval scores. The infrastructure that makes this safe already runs our own products, which is why our integrations move fast.
- Your app → AI layer · auth context forwarded 200
- Input validated · payload clean PASS
- Retrieved 4 docs · reranked · scoped to user's permissions PASS
- Generated with citations · confidence 0.94 PASS
- Feature flag: ai_summary · live for 12% of tenants STAGED
How Bolted-On AI Breaks
Adding AI to a working product fails differently than building one from scratch. These six patterns recur across integrations we get called in to repair.
LLM Called Raw From Production
A developer wires an API key into a controller and ships it. No retries, no fallbacks, no input validation, no timeout policy. The first malformed payload or provider outage takes the feature down with it.
Data Boundaries Bypassed
The AI layer queries the database directly because it was faster to build. Now it reads rows the calling user was never allowed to see, and nobody noticed until a customer did.
Glued to the UI, Not the Workflow
A chat panel bolted to the sidebar because "we need AI somewhere." It answers questions nobody asked and takes no actions, so usage flatlines and the feature gets quietly removed next quarter.
The Token Bill Surprise
No caching, no model routing, no per-tenant cost attribution. The feature works beautifully until the invoice arrives, and then finance kills a capability customers were starting to rely on.
No Way to Turn It Off
The AI shipped in the same deploy as everything else, with no flag and no rollback path. When it misbehaves, the options are an emergency release or an apology email.
Demo Worked, Production Didn't
The feature was evaluated on the ten cases in the demo script. Real traffic brought accents, malformed inputs, and edge cases nobody measured. Without an eval harness, every release is a coin flip.
Does It Even Need AI?
Roughly half the features we get asked to build would work better as a deterministic rule, a SQL query, or a conventional workflow, and we say so. AI earns its place where the work needs language, reasoning, or pattern recognition over unstructured data.
Reach for AI when the input is…
- Unstructured: documents, emails, tickets, conversations
- Judgment-dependent: classify, summarize, extract, reconcile
- Variable in shape: formats and phrasing that change weekly
- Cross-referential: answers assembled from several sources
Stay deterministic when the work is…
- Rule-expressible: if it fits in a lookup table, use one
- Exact-match: identifiers, thresholds, arithmetic
- Compliance-fixed: outputs that must be identical every time
- Latency-critical: paths where 600ms of inference is too slow
The feasibility study runs this check on every candidate feature. If a workflow fits better than a model, the recommendation says workflow.
Six Ways AI Docks Into a Running Product
The right pattern depends on where the value lives: in your data, in your workflows, or in your users' hands. Most products need one or two, not all six.
API-First Model Embedding
A model-backed feature your backend calls through a clean API layer: structured prompts, retries, fallback chains, and cost controls from day one. Your codebase keeps its shape.
Retrieval Over Your Data
Hybrid vector and knowledge-graph search over your documents and records, so answers cite your sources with access scoped per user. RAG development →
In-App Copilots
Assistants that live inside your product, call your APIs for the facts, and take actions with a human confirm step where it matters. Chatbot development →
AI Steps in Existing Workflows
Classification, extraction, summarization, and routing dropped into the business processes you already run, not a parallel tool to adopt. Workflow automation →
Model Orchestration
Different tasks routed to different models on cost, latency, and accuracy, with A/B testing between providers and eval-driven promotion instead of loyalty to one vendor.
Semantic Search Upgrade
Replace the keyword search bar with intent-aware retrieval: vector plus exact-match, reranked, permission-filtered. A drop-in upgrade your users notice immediately.
How an Integration Lands
Five steps per capability. The roadmap keeps shipping while the AI lands, because nothing touches production until it has earned its eval score.
Surface Mapping
WEEKS 1-2 · INSIDE THE STUDYWe map the surfaces the AI layer will touch: your APIs, auth provider, event bus, and data boundaries. The output is an integration contract, what the AI reads, what it can write, and what it must never see.
Capability Build
ONE CAPABILITY AT A TIMEThe feature is built as a middleware layer your backend calls: prompts versioned, fallbacks defined, cost controls in place. Your codebase keeps its architecture; the AI sits next to it.
Eval Gate
BLOCKS: DEMO-ONLY FEATURESNothing ships on a demo script. A golden-set eval harness scores the capability on real cases drawn from your traffic, and it does not ship until it clears the bar.
Flagged Rollout
BLOCKS: BIG-BANG RELEASESEvery capability goes live behind a feature flag: a few tenants first, then a widening percentage while we watch the numbers. Any capability can be revoked in seconds without a deploy.
Observation & Handover
YOURS TO RUNAn observation window on live traffic tunes thresholds before handover. You get the integration contract, runbook, eval harness, and dashboards, in your accounts, with full IP assignment.
Systems We Wire Into
The AI layer plugs into the surfaces your product already exposes. No replatforming, no new tool for your team to learn.
The integration surface
- Your backend's REST or GraphQL APIs: the AI reads and acts through them, not around them
- Your auth provider: permissions enforced per user, so the AI never sees what the caller cannot
- Your event bus and webhooks: AI steps triggered by the events you already emit
- Your data stores: Postgres, warehouses, document stores, searched in place or indexed
- CRM and revenue tools: Salesforce, HubSpot, and the systems your team already lives in
- Helpdesk and support platforms: ticket context, macros, and escalation paths
- Internal tools and admin panels: copilots embedded where the work happens
- Your CI/CD and feature-flag system: capabilities roll out through your release process
Why Our Integrations Ship Faster
Most integrations assemble validation, evals, and observability from scratch. Ours start from the infrastructure that already runs our products and our banking agent in production. That is where the speed comes from.
What every call clears
- Input validation: injection and malformed payloads rejected at the boundaryEVERY CALL
- Scoped permissions: the AI sees only what the calling user can seeEVERY CALL
- Retrieval grounding: hybrid vector and knowledge-graph search over your dataEVERY QUERY
- Confidence routing: below threshold falls back or escalates to a humanEVERY RESPONSE
- Eval harness: golden-set scores gate every release, in CIEVERY RELEASE
- Feedback loop: failures and overrides feed the next eval setCONTINUOUS
- Governance layer: policy enforcement, audit log, kill switch, feature flagsALWAYS ON
- Operations: rollback runbooks, regression checks on model updatesALWAYS ON
- Observability: every call traced, cost-attributed, and loggedCONTINUOUS
What You Own at Launch
A working AI capability inside your product and everything needed to operate, extend, and audit it, in your accounts from day one.
The launch handover
- AI capability live in your product, behind a feature flag you control
- Integration contract: the surfaces, data boundaries, and permissions the AI layer uses
- Input validation layer: injection and malformed payloads screened at the boundary
- Scoped permissions and policy enforcement on every AI call
- Model routing and fallback chains, with provider failover configured
- Eval harness running in your CI, gating every release
- Feedback loop: failures and overrides feed the eval set
- Observability: traces, dashboards, and alerts on every call
- Operations runbook: rollback, regression checks, incident response
- Cost model and per-call attribution, so the invoice never surprises you
- Post-launch observation window on live traffic
Scoped by a Feasibility Study, Fixed at Kickoff*
The study maps your surfaces, runs the fit test on candidate features, and prices the first integration. The build ships one capability at a time behind flags. After launch, you choose what continues.
Feasibility Study
Maps your product's surfaces and data boundaries, runs the AI-vs-deterministic fit test on your candidate features, and prices the first integration. Ends with a go or no-go verdict and a costed estimate.
The Integration
Scope, timeline, and fee locked at kickoff. Weekly demos show the capability running against your real data, behind a flag, until eval scores and launch criteria pass.
Widen or Hand Over
An observation window on live traffic is included. Then a retainer to integrate the next capabilities and widen the flags, or a clean handover with the contract, harness, and runbook.
*Build pricing depends on scope and is confirmed in the feasibility study. The number is locked before work starts.
Raw API Calls, Integration Agencies, and Operator Engineers
AI integration splits between a developer wiring in an API key, agencies that ship a demo, and teams that run AI in production. The difference shows up on the first real traffic.
Straight into a controller, with the next deploy.
A demo branch that becomes someone else's problem.
Behind a feature flag, eval-gated, one capability at a time.
Whatever the query happened to return.
Scoped in the spec document.
Enforced at runtime: the AI sees only what the caller can see.
Emergency release or apology email.
A support ticket and a change order.
The flag goes off, the fallback takes over, the eval set grows.
Discovered on the invoice.
Estimated at proposal time.
Modeled per call before commit, attributed per feature after launch.
It worked on my machine.
Portfolio screenshots.
Agents running ~500K actions/month in PCI-DSS production.
| Dimension | Raw API Call | Integration Agencies | FLYTEBIT |
|---|---|---|---|
| How it ships | Straight into a controller, with the next deploy. | A demo branch that becomes someone else's problem. | Behind a feature flag, eval-gated, one capability at a time. |
| Data boundaries | Whatever the query happened to return. | Scoped in the spec document. | Enforced at runtime: the AI sees only what the caller can see. |
| When it breaks | Emergency release or apology email. | A support ticket and a change order. | The flag goes off, the fallback takes over, the eval set grows. |
| Cost | Discovered on the invoice. | Estimated at proposal time. | Modeled per call before commit, attributed per feature after launch. |
| Proof | It worked on my machine. | Portfolio screenshots. | Agents running ~500K actions/month in PCI-DSS production. |
Match the Tool to the Question
Integration answers how AI lands inside a product that already ships. If that is not your question, one of these fits better.
Frequently Asked Questions
What does an AI integration engagement include?
Mapping your product's surfaces and data boundaries, designing the AI layer that sits alongside your backend, and shipping one capability at a time behind feature flags. The deliverable is a working AI feature inside your existing product with evals, observability, and a runbook, not a demo wired to an API key.
Do we need to rebuild our product to add AI?
No. The AI layer docks against the surfaces you already expose: your REST or GraphQL APIs, your auth provider, your event bus, your data stores. Your codebase keeps its shape. Each capability ships behind a flag, so you can widen or revoke it without a deploy.
How do you decide whether a feature needs AI at all?
The feasibility study includes a fit check. Roughly half the features clients ask for would work better as a deterministic rule, a SQL query, or a conventional workflow, and we say so. AI earns its place when the work needs language understanding, reasoning, or pattern recognition over unstructured data.
What happens when the AI feature gets it wrong?
Every call is traced and confidence-scored before it ships a response. Low-confidence cases fall back to a deterministic path or route to a human with context, and failures feed the eval set so the next release handles that class of input. The feature flag means you can roll any capability back in seconds.
How do you control token and inference costs?
Cost is engineered, not discovered on the invoice: model routing sends each task to the cheapest model that passes the eval bar, caching deduplicates repeated calls, and every call is cost-attributed per feature and per customer. The feasibility study produces a per-call cost model before you commit.
How long does an integration take?
A first capability typically ships in 4 to 8 weeks depending on your stack's surfaces and risk profile. The retrieval, eval, and observability infrastructure already exists from our own products, so builds start from proven components. The exact window is set in the feasibility study.
How much does AI integration cost?
Cost depends on scope: number of capabilities, surfaces touched, and governance requirements. The feasibility study, which starts from $2K, produces a costed estimate. The build price is then fixed at kickoff, so the number is agreed before work starts.
Ship the Feature, Keep the Codebase
Schedule a 30-minute working session with our expert team. We will look at the AI features on your roadmap and give you a straight answer on which ones are worth building first, and which need a SQL query instead.