SaaS Development · for AI product founders
SaaS Development for AI Agent & RAG Products
In short
Aqib Ops ships production AI SaaS in 8–14 weeks. We build the unsexy infrastructure that turns a Jupyter prototype into a real product: retrieval pipelines with evals, agent orchestration with retries, per-tenant cost controls, prompt versioning, and observability.
The problem
Most AI products are demos that don't survive contact with real users: hallucinations in the wrong place, runaway token costs, no way to A/B test prompts, no way to debug a bad answer two days later. Demos win seed rounds; infrastructure wins retention.
Our approach
We treat prompts as code (versioned, evaluated, deployed), retrieval as a search problem (with hybrid search and reranking), and agents as workflows (durable, observable, cost-capped). Every LLM call is logged, every output is evaluable, every tenant has a budget.
Stack we'd reach for
Vercel AI SDK / LangChain
Streaming, tool-calling, and provider abstraction without lock-in.
pgvector + hybrid search
Retrieval that beats naive cosine similarity on real corpora.
Inngest or Trigger.dev
Durable agent runs that survive Vercel's 10s function limit.
Braintrust or Langfuse
Eval datasets, prompt versioning, and trace-level debugging.
Cloudflare Workers + R2
Cheap, fast retrieval edge for global RAG products.
What you'd get
- Hybrid retrieval pipeline with reranking and evals
- Agent orchestration layer with retries + cost caps
- Prompt versioning and A/B testing infrastructure
- Per-tenant token budgets and rate limits
- Trace-level observability (which prompt → which output)
- Eval harness with golden datasets and CI checks
Recent example
A legal-research AI startup
Replaced a flaky LangChain notebook with a production agent that handles 4k queries/day. Hallucination rate dropped from 12% to 1.8% after we added a reranker and contradiction check.
Frequently asked
What does it cost to build an AI SaaS product?
Most production AI SaaS we build lands between $40k and $120k for the initial product. Ongoing token cost is the bigger long-term line item — we set up budgets and caching from day one to prevent runaway bills.
How do you prevent hallucinations?
Hybrid retrieval (vector + keyword), reranking, and a contradiction check before responding. We instrument every output so you can see which retrieved chunk produced which answer — debuggable RAG, not magic.
Which LLM providers do you support?
OpenAI, Anthropic, and open-source via Together or Replicate. We design with provider abstraction so you can swap or A/B test models without rewriting the product.
How do you handle eval and prompt regression?
We set up Braintrust or Langfuse with a golden dataset of 50–200 representative queries. Every prompt change runs against the eval suite in CI before merge.
Can you integrate with my existing data warehouse?
Yes. We sync from Snowflake, BigQuery, Postgres, or S3 into pgvector via scheduled Inngest jobs, with incremental upserts so re-indexing stays cheap.
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