Artificial Intelligence and Data
AI model integration
Integrating a model is not just pasting an API key into the backend and hoping: it means timeouts, queues when the provider stalls, per-user caps so budgets do not explode, caching for identical prompts, friendly errors when the model refuses policy, and plan B (another model or a fixed message) when latency exceeds what users tolerate. Viscale implements this product layer—internal gateway, stable contracts for mobile or web apps, token and error telemetry, and contract tests that run before every deploy. If you trained or self-host a model, we connect it with the same discipline as any critical microservice.
We start from the contract: what input the product sends (text, image, JSON), what output it expects, and how many seconds before the user gives up. We version prompts and parameters with code so a “Friday deploy” does not silently change behavior. For teams comparing vendors, we add percentage routing or feature flags without rewriting screens.
What we ship in practice
Single internal gateway
Apps call your API; it picks the provider and applies shared policy.
Token streaming to the front end
Word-by-word responses with cancellation if the user leaves the screen.
Queue for marketing spikes
A viral campaign does not flatten the cluster; jobs degrade gracefully.
A/B routing across models
Measure quality and cost in parallel before committing 100%.
Embeddings for semantic search
Pipeline that indexes and refreshes vectors without blocking the main app.
Self-hosted model endpoint (vLLM, etc.)
Health checks, minimum autoscaling, and alerts when GPUs saturate.
Input and output moderation
Internal blocklists plus a light classifier before and after the large model.
Cheap overnight batch
Summarize thousands of tickets using a batch API when the vendor offers one.
Typed function-calling layer
The model only calls functions you exposed with validated JSON schema.
Migration across regions or vendors
Cutover plan with feature flags and one-click rollback.
Security: keys only in a vault, rotation, and a list of what must never go to a public cloud. For sensitive data we evaluate providers with the right agreements or models inside a VPC. We document provider rate limits and implement exponential backoff to avoid cascading failure during outages.
Product teams get a simple dashboard: calls per day, p95 latency, estimated cost, and fallback rate—to decide whether to raise limits or switch models. When a new model hits the market, the swap happens at the integration layer, not across fifty scattered files.
Portfolio of AI model integration
Deliverables
Production gateway
Stable URL consumed by your services or apps.
OpenAPI specification (or similar)
Public contract for internal teams.
Versioned configuration
Prompts, models, and limits in the repository.
Usage dashboards
Calls, latency, errors, and estimated cost.
Incident runbook
Provider down, quota exceeded, slow degradation.
Data handling policy
What may leave the perimeter and log retention.
Automated tests
Wired into the deploy pipeline.
Developer onboarding guide
How to get internal keys and debug a bad call.
Model migration plan
Steps and rollback criteria.
Handoff session
Platform team takes ownership with clarity.
Security checklist
Items checked before opening a new flow.
Optimization suggestions
Next increments based on the first weeks live.
Execution methodology
-
Define the API contract
Input, output, timeouts, and error codes from the product perspective.
-
Provider selection
Data requirements, latency, and cost per million tokens.
-
Implement gateway and policies
Rate limits, authentication, and quotas per tenant or user.
-
Secrets and compliance
Vault, rotation, and DPA checks when applicable.
-
Resilience and fallback
Second model, queue, or stable message during outages.
-
Observability
Metrics, traces, and logs correlated with customer requests.
-
Contract and load tests
Simulate spikes and large payloads before launch.
-
Developer documentation
OpenAPI or equivalent with sample calls.
-
CI regression suite
Stable outputs on reference prompts.
-
Gradual go-live
Percentage rollout or beta list until confidence is high.
-
Post-launch cost review
Tune cache, context size, and alternate models.
