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. This page is an anonymized composite of recurring implementation patterns. Figures are representative internal examples, not independently audited results or guarantees.
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.
Representative Outcomes
These internal examples illustrate how success can be measured. Actual outcomes depend on baseline performance, data quality, integration scope, operating controls, and user adoption.
₹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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