AI integration
AI integration services for software and SaaS
We add AI to the software you already run: LLM features inside your product, answers grounded in your own data, and AI steps inside existing workflows. Every integration comes with an evaluation set, guardrails against prompt injection and data leaks, and cost and latency budgets that hold up in production.
When an AI integration makes sense
Your team copies information between documents, tickets and systems and summarizes it by hand.
Customers ask the same questions your documentation already answers.
A competitor shipped an AI feature and your roadmap needs a credible answer.
You built an AI prototype, but its answers are inconsistent and nobody can say whether a change made it better.
Model costs or latency grew faster than usage.
Which integration pattern fits
| Pattern | Use it when |
|---|---|
| Pattern01A direct LLM call with structured output | Use it whenThe task is self-contained: classify, extract, draft or summarize text you already have. |
| Pattern02Retrieval-augmented generation (RAG) | Use it whenAnswers must come from your own documents, catalog or knowledge base, with citations. |
| Pattern03Tool use (function calling) | Use it whenThe model needs live data or must act in your systems through defined, permission-scoped tools. |
| Pattern04An agent or multi-step workflow | Use it whenThe job needs several steps and decisions; we add approvals and limits for anything risky. |
| Pattern05Fine-tuning | Use it whenOnly after prompting and retrieval are measured and a consistent format or style still can't be reached. |
AI integration project
A scoped project that takes one AI feature from idea to production inside your software.
What we do
- Choose the first use case and define what good output looks like
- Build an evaluation set from real examples
- Compare models and approaches on quality, latency and cost
- Build the integration: data access, retrieval, prompts, structured outputs and fallbacks
- Add guardrails, logging, monitoring and cost controls
What you get
- The feature in production, behind a flag you control
- The evaluation suite running in CI
- Dashboards for quality, cost and latency, and documentation for your team
Scoped after a call; the estimate depends on the integrations and expected usage.
Guardrails in every integration
AI features touch your data and your customers, so safety is part of the build, not an afterthought.
- 01
Prompt-injection defenses and strict separation of instructions from untrusted content
- 02
Least-privilege access to data and tools, with human approval for risky actions
- 03
Provider settings for data retention and regional processing chosen for your requirements
- 04
Output validation against schemas before anything reaches users or systems
- 05
Logs and audit trails so every AI decision can be reviewed
How it works
- 01
Scope
The use case, success metric, data sources and constraints.
- 02
Evaluate
An evaluation set and a comparison of models and patterns.
- 03
Build
The integration with guardrails, monitoring and cost controls.
- 04
Launch
A flagged rollout, real-usage review and handover.
AI integration questions
Engineering work that connects AI models to your existing software and data: LLM features inside your product, retrieval over your documents, and AI steps inside workflows, with the evaluation, security and monitoring around them.
Usually not at first. Most integrations reach their quality target with good prompts, structured outputs and retrieval over your data. Fine-tuning comes in only when those are measured and still fall short on format or style.
It depends on the task, data-handling requirements and cost. We test candidate models from providers such as OpenAI, Anthropic and Google against your evaluation set and recommend one per task.
Yes, with the right design: least-privilege data access, provider retention and region settings that fit your requirements, and defenses against prompt injection. We plan UK and EU data handling with GDPR in mind.
With an evaluation set built from real examples and pass criteria agreed before building. It runs in CI, so every prompt or model change is measured before release.
Scope an AI integration
Tell us what the feature should do, which systems and data it needs, and where it will live. We'll reply with next steps.
Prefer email? Write to webguru.com.pk@gmail.com