Add AI to Existing Products & SaaS
Embed custom AI chatbots, semantic search, and smart summarization directly into your current web app or SaaS.
Fixed milestone scope • No hourly surprises • Direct GitHub delivery
For multi-tenant databases, custom fine-tuning, complex data isolation, or dedicated ongoing AI feature sprints.
Why Typical Approaches Fail & How We Engineer It
Real engineering discernment: diagnosing root failure modes and engineering resilient solutions instead of generic AI wrappers.
Most startups attempt to add AI by slapping generic OpenAI API wrappers into their application. This leads to hallucinated answers, unpredictable token billing spikes, sluggish response times, and angry customers when private tenant data leaks.
We build a hardened, production-grade retrieval pipeline: (1) Chunking and indexing data in PostgreSQL using pgvector with 3072-dimensional Gemini embeddings, (2) Enforcing strict cosine distance boundaries (<=0.5) to reject irrelevant queries, (3) Streaming responses via Groq/Gemini with fallback models, and (4) Rate-limiting by IP and user ID to prevent runaway costs.
Technical Architecture Blueprint
[User Query / Client Application]
│
▼
[Next.js API Handler] ──▶ [Rate Limiter & Token Budgeting]
│
▼
[Gemini 2.0 Text Embedding] ──▶ (3072d Dense Vector)
│
▼
[PostgreSQL + pgvector Cosine Index]
│
├─▶ Cosine Distance <= 0.5 ──▶ [Grounded Context Assembly] ──▶ [Groq Stream LLM] ──▶ Verified Output
│
└─▶ Cosine Distance > 0.5 ──▶ [Deterministic Fallback Chain] ─────────────────────▶ Declines HallucinationExact Deliverables & Scope
Clear scope boundaries eliminate surprises. Here is exactly what is delivered in a standard sprint versus what is handled under custom scope.
- Integration with up to 2 existing database entities or document collections
- Full pgvector setup in PostgreSQL with cosine distance indexing
- Streaming UI component (React/Next.js) or REST API endpoint
- Rate limiting and token budget guards
- 14-day post-launch bug warranty
- •Multi-tenant data partitioning across hundreds of enterprise tenants
- •Automated document sync pipelines with Google Drive, Notion, or Slack
- •Dedicated model fine-tuning or hybrid local LLM hosting
- •Ongoing monthly prompt optimization and evaluation retainer
Technologies Implemented in This Service
How This Sprint Is Executed
Discovery & Code Audit
Understand your product goals, inspect your existing codebase or requirements, and define a clear milestone roadmap with fixed deliverables.
Rapid Prototype & Validation
Build and validate a working prototype or targeted fix to test assumptions, AI response quality, and API contracts early with your team.
Implementation & Hardening
Refine business logic, implement caching and error handling, optimize database queries, and test edge cases thoroughly with regression tests.
Production Handover & Support
Deploy to production, integrate with your existing stack, and provide complete documentation, tests, and code walkthroughs.
Frequently Asked Questions About This Sprint
Do we need to rewrite our application to add AI?
No. We connect directly to your existing PostgreSQL database, backend routes, and frontend UI via lightweight REST or streaming API endpoints. Your existing architecture remains untouched.
How do you guarantee the AI won't hallucinate fake information?
We use strict cosine distance thresholds (<=0.5) against pgvector embeddings. If the user's question has no semantically relevant documents in your knowledge base, the system deterministically replies that it does not know instead of guessing.
How are API costs controlled?
We implement per-user rate limiting, strict token budgeting per request, and hybrid prompt caching to keep LLM operational costs under pennies per day.
Ready to Build Add AI to Existing Products & SaaS?
Tell me about your tech stack and target timeline. I review requirements and provide a clear milestone scope within 24–48 hours.
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