Industries

AI for SaaS & Technology

AIYatch helps SaaS and technology companies ship AI inside the product: in-app copilots, RAG-powered help centres, onboarding assistants, AI features exposed through your API, and churn and usage analytics. We work as an extension of your engineering team, delivering production-grade code, evaluation suites and monitoring so AI features are reliable, measurable and cost-controlled.

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Typical challenges

Product teams face pressure to add AI features quickly without a clear roadmap or evaluation method.
LLM costs, latency and hallucinations are hard to control at scale.
Support and onboarding load grows faster than the customer-success team.
Choosing between hosted models, open-source models and fine-tuning is unclear.

Typical use cases

In-app copilots and AI features

Assistants embedded in your product that answer questions, generate content or take actions through your own APIs, with usage metering and guardrails.

RAG help centre and support deflection

A retrieval assistant over docs, tickets and release notes that resolves common questions in-app and hands complex cases to support with context.

Onboarding and activation assistants

Guided assistants that help new users configure the product, import data and reach first value, tracked against activation metrics.

Churn prediction and usage analytics

Models over product telemetry that identify at-risk accounts and expansion signals for customer-success and sales teams.

AI API and SDK development

Backend pipelines, orchestration layers and SDKs that let your customers and partners use your AI capabilities programmatically.

LLM evaluation, cost and latency optimisation

Evaluation harnesses, model routing, caching and quantised open-source alternatives that reduce cost while keeping quality measurable.

Questions from this sector

Many clients do both. We often build the first version, the evaluation harness and the deployment pipeline, then hand over to your team with documentation and pairing sessions. Others keep us as an embedded specialist team. Either way, code lives in your repositories from day one.

We meter usage per tenant, route simple requests to smaller models, cache repeated prompts and retrievals, cap context length, and evaluate whether a fine-tuned or open-source model can replace a frontier model for specific tasks. Cost per feature becomes a tracked metric, not a surprise on the invoice.

We build an evaluation set from real product scenarios, score outputs automatically and with human review, and run it in CI so regressions are caught before deployment. In production, monitoring tracks quality, latency and cost with alerts, and a limited beta with selected customers precedes general availability for anything user-facing.