Discipline Specification // AI INTEGRATION

AI Integration & LLM Features for SaaS Products

Adding AI to a product is not calling the OpenAI API and streaming a response into a text box. The interesting problems are retrieval — making the model answer from your data rather than its training set — context management, cost control, latency, and failure modes. SynS Infrastructure has published research on retrieval-augmented generation and has built production AI features including voice interfaces. The work starts from what your product actually needs, not from what a demo looks like.

DISCIPLINE BLUEPRINT TELEMETRY
100%
SOURCE CODE OWNERSHIP
SLA
24H DIRECT FOUNDER COMM
CORE STACK FOR THIS SERVICE
OpenAI / Anthropic / Geminipgvector (Supabase)LangChain / custom pipelineWeb Speech APINext.js Server Actions
DELIVERY COMMITMENT

Continuous staging previews from Sprint 1 with clean architectural handovers.

Target Alignment

Who this is for

  • 01A SaaS founder who wants a chatbot or Q&A feature that answers from their product's own data — not from GPT's general knowledge.
  • 02A team building a voice interface: voice-in, processed-out, spoken response — working end-to-end.
  • 03A product that generates content (reports, summaries, drafts) from structured data and needs it to be reliable, not hallucinated.
Deliverable Scope

What's included

  • ✓RAG (Retrieval-Augmented Generation) pipeline: document ingestion, chunking, embedding, retrieval, and cited response
  • ✓LLM integration via OpenAI, Anthropic, or Gemini — with fallback logic and cost monitoring
  • ✓Vector database setup (pgvector on Supabase, or Pinecone/Weaviate for larger scale)
  • ✓Voice interface: Web Speech API for input, TTS for output — browser-native, no third-party SDK required for basic flows
  • ✓Streaming responses — perceived latency matters as much as actual latency
  • ✓Context window management — prompt engineering that doesn't blow the token budget
  • ✓Evaluation setup: how to measure whether the AI is actually answering correctly, not just confidently
Engineering Stack

Stack & tooling architecture for AI Integration & LLM Features for SaaS Products.

Why we choose each component in the production runtime.

OpenAI / Anthropic / Gemini

Model choice depends on the task — cost, context window, and multimodal requirements all factor in

pgvector (Supabase)

Vector similarity search inside Postgres — no separate vector DB needed for most products

LangChain / custom pipeline

LangChain for standard RAG patterns; custom pipelines when the abstraction gets in the way

Web Speech API

Browser-native voice I/O — no third-party SDK, no extra cost per request for basic voice features

Next.js Server Actions

Streaming AI responses via Server Actions — no separate API layer needed

Engagement Protocol

How this discipline executes.

01

Scope & Architecture

Define exactly what gets built, what doesn't, and milestone criteria — in writing, before work starts.

02

Iterative Staging

Weekly written updates with staging deployments from the first sprint. Test features as they are built.

03

Handover & Ownership

100% repository, credentials, and documentation handed over. Retainers available for ongoing evolution.

Technical Clarifications

Questions & answers for this discipline.

Can the AI answer from our own docs/database?+

Yes — that's what RAG is for. Documents are ingested, chunked, and embedded. At query time, the most relevant chunks are retrieved and sent to the model as context. The model answers from your data, with citations.

How do you prevent hallucinations?+

RAG significantly reduces hallucination by grounding responses in retrieved context. On top of that, the system can be configured to refuse to answer questions where no relevant context was retrieved — rather than guessing.

Is this built into my existing product or as a separate service?+

Both patterns work. For most products, integrating into the existing codebase is cleaner — no separate service to deploy and maintain. We'll discuss the trade-offs in the scoping call.

What does this cost to run per month?+

It depends entirely on usage — model, token count, and request volume. We'll model the cost based on your expected usage during scoping, with optimisations (caching, smaller models for simpler tasks) built in from the start.

DISCIPLINE INTAKE

Ready to engineer your AI Integration & LLM Features for SaaS Products?

Direct founder collaboration with milestone transparency. We reply within 1 business day.

Start This Project →