Add AI to Your Product
Ship an AI feature your customers trust and your team can maintain
You have a live product and a real use case, and you need the feature to work reliably rather than demo well.
What we deliver
- AI feature deployed behind a feature flag in your product
- Evaluation set, scoring harness and baseline results you own
- Model gateway with provider fallback, caching and budget controls
- Guardrail layer with output validation and refusal handling
- Cost, latency and quality telemetry with alert thresholds
- Data flow documentation for security and legal review
Common challenges
What tends to go wrong
These are the failure patterns we see most often in this situation — and what our approach is designed to avoid.
The demo does not survive contact with users
Real inputs are messier than test inputs. Without validation and fallbacks, quality drops the moment the feature reaches people who were not in the room when it was built.
No way to tell whether a change helped
Prompt tweaks get shipped on vibes because there is no test set. Quality then moves unpredictably in both directions.
Costs that scale worse than revenue
Per-request spend that looks trivial in testing becomes a line item nobody forecast once the feature is used at volume.
Security and legal cannot approve what they cannot see
Unclear data flows and retention terms stall a feature that is otherwise ready to ship.
Recommended approach
How we would run it
Start with the evaluation set
Before any prompt work, we build a representative set of inputs with expected outputs. Quality becomes a number, and every change is measured against it.
Build inside your codebase
The feature lives in your repository, follows your patterns and ships through your existing release process. No parallel system to integrate later.
Guardrails before rollout
Schema-validated outputs, input filtering, tool allow-lists, human approval for irreversible actions, and a documented fallback when the model is unavailable.
Cost and quality on one dashboard
Per-feature spend, latency and quality scores are visible together, with budget alerts, so nobody is surprised by the invoice or the regression.
Deliverables
What you receive
- AI feature deployed behind a feature flag in your product
- Evaluation set, scoring harness and baseline results you own
- Model gateway with provider fallback, caching and budget controls
- Guardrail layer with output validation and refusal handling
- Cost, latency and quality telemetry with alert thresholds
- Data flow documentation for security and legal review
- Runbook and enablement session for your engineers
AI feature architecture inside an existing product
A thin gateway isolates model providers from your application, so evaluation, caching, cost control and fallback all have one place to live.
- Product surfaceFeature in your existing UI, flag-controlled
- AI gatewayRouting, caching, budgets, provider fallback
- RetrievalPermission-aware index over your content
- GuardrailsSchema validation, filtering, approvals
- EvaluationOffline scoring plus live feedback capture
- TelemetryCost, latency and quality per feature
Delivery roadmap
The sequence of work
Step 1 — Use case definition
Pick one feature, define what good output looks like, and agree the quality bar for release.
Step 2 — Evaluation baseline
Assemble the test set, score a naive implementation, and establish the number to beat.
Step 3 — Build and iterate
Retrieval, prompting and validation improved against the evaluation set, not against impressions.
Step 4 — Production hardening
Gateway, guardrails, caching, rate limits, telemetry and the documented fallback path.
Step 5 — Staged rollout
Internal users, then a customer cohort, then general availability — with quality and cost watched at each gate.
Indicative timeline
Roughly how long this takes
Indicative only. Timings assume reasonable availability for decisions and access to the systems involved — we confirm a specific plan after discovery.
| Phase | Indicative duration | What affects it |
|---|---|---|
| Definition and evaluation setup | About 1–2 weeks | Depends on data access and how clear the quality bar is |
| Build and iteration | Typically 4–8 weeks | Varies with retrieval complexity and integration surface |
| Hardening and rollout | About 2–3 weeks | Includes staged release and monitoring |
Engagement model
How this work is usually structured
Fixed-scope project for a first feature, or team extension when your engineers are building alongside us.
Compare engagement modelsRelated
Where to go next
- HRTechSample content
An applicant tracking platform with explainable candidate matching
A recruitment platform where structured CV parsing and explainable matching cut screening time without removing recruiter judgement.
- Next.js
- TypeScript
- Node.js
- PostgreSQL
AI & Automation
Applied AI engineering — assistants, agents, document workflows and automation — built on your data with evaluation and guardrails from day one.
Explore AI & AutomationProduct Engineering
SaaS platforms, custom software and MVPs engineered for multi-tenancy, billing, security and the release cadence a growing product needs.
Explore Product Engineering
Questions
Add AI to Your Product — questions we are asked
Add AI to Your Product
Book a consultation
Thirty minutes with an engineer who has done this before. You leave with an approach, whether or not you engage us.
Prefer email? contact@xalicon.co