A Bengaluru B2B SaaS Team Replaced $2,400/mo of AI Coding Subscriptions with a Custom AI Layer
Custom AI development accelerator trained on the company's own microservices codebase and API contracts — replacing 12 third-party AI coding subscriptions and cutting PR cycle time from 3.2 days to 2.1 days.
PR review time
3.2 → 2.1 days
31% faster
Industry
B2B SaaS
Location
Bengaluru, Karnataka
Timeline
8 weeks to pilot, 12 weeks to full rollout
Client: A 38-person product engineering team at a B2B SaaS company in Bengaluru. Client name withheld per engagement agreement — industry, scale, and outcomes are verbatim.
Key Facts
- Industry
- B2B SaaS — DevOps / Engineering tooling
- Location
- Bengaluru, Karnataka, India
- Problem
- $2,400/month spend on multiple AI coding subscriptions with overlapping capabilities
- Solution
- Custom AI coding layer tuned to the team's stack and codebase
- Timeline
- 60 days to first production rollout
- Headline outcome
- $2,400/month subscription cost replaced by a single owned system
- Productivity
- Per-engineer task throughput up materially in the first 30 days
The Challenge
- ▸12 active Claude Code and Cursor seats at ₹14,800/dev/month — ₹1.77 lakh in monthly AI tooling spend.
- ▸Engineers reported ~40% of suggestion-review time was spent correcting outputs that had no context of the company's internal microservices architecture.
- ▸Three senior engineers flagged this repeatedly in sprint retrospectives; leadership wanted a data-backed build-vs-buy answer, not just another subscription migration.
- ▸Compliance constraints: source code could not leave controlled infrastructure, ruling out several vendor-managed options.
What We Built
- 1Built a retrieval-augmented generation (RAG) pipeline over the company's private Git repositories, internal API contracts, and architecture decision records.
- 2Fine-tuned a mid-size open-weights code model on the team's own patterns so suggestions reflect their service boundaries and naming conventions — not public GitHub averages.
- 3Deployed on the company's own GPU infrastructure with full audit logging and per-developer usage dashboards.
- 4Integrated into existing IDE (VS Code) and CI pipeline so suggestions surface where engineers already work.
- 5Ran a 4-week pilot with 6 engineers against a control group of 6 on the old subscription stack — then rolled out to the full team based on measured outcomes.
Measured Outcomes
₹1.77 L → ₹68 K
Monthly tooling spend
62% reduction including compute
3.2 → 2.1 days
Average PR review cycle
31% compression, measured over 8 sprints
72%
Accepted-suggestion rate
Up from ~45% with generic tools
0
Code leaving controlled infrastructure
Compliance requirement satisfied by design
Technologies Deployed
- Custom GPT fine-tuning on private repositories
- Vector database for RAG over internal documentation
- On-prem GPU deployment (2x A100)
- VS Code extension + CI-triggered code review
Services Used
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