Qualcomm deployed Context to power AI-assisted support across their global engineering organization. Within four months, they achieved measurable results that exceeded their four-year transformation targets — not by using a smarter model, but by giving their existing model access to institutional knowledge.
The challenge
Qualcomm’s customer engineering teams support hundreds of global customers daily across dozens of SDKs and product lines. Their AI-assisted support was running at less than 23% accuracy using a leading foundation model — because the model had no access to proprietary specifications, internal documentation, help center content, or institutional knowledge accumulated over decades. The model was smart but completely blind to the company’s reality.
The solution
Deployment: full VPC deployment with SSO-based permissions — zero data egress
Hybrid indexing: help center documentation (Index & Embed), proprietary source code (Virtual Directory — metadata only), 100+ Git repos mounted into ContextFS
Unified context: a unified ContextFS repository serving customer engineering, business development, marketing, test engineering, and DevOps teams
Continuous learning: sleep-time compute running continuously to write memories, update tags, and propagate learnings across all agent deployments
The results
| Metric | Before Context | After Context |
|---|---|---|
Query accuracy | < 23% | 98% (same model, same parameters) |
Team adoption | 1 team (10 people) | 85 teams (2,500+ engineers) |
Automated workflows | 5 | 1,600 |
Productivity impact | Baseline | 40% increase (4 months in) |
Why it worked
The accuracy jump from 23% to 98% came entirely from surfacing the right context at runtime — proprietary specs, internal documentation, historical support interactions, and learned memories. No model change. No fine-tuning. The same foundation model went from failing to production-grade simply by having access to institutional knowledge through ContextFS.
Why it scaled
Once the knowledge infrastructure was deployed, adding new teams became trivial. Each new team connects their data sources, defines their agents and workflows, and starts working — no bespoke engineering required. Onboarding a new team takes 1–2 days.
The original deployment target was a 20% productivity improvement over four years. At month four, the measured result was 40% — twice the target in a twelfth of the time.
Infrastructure is the hard part. Once Context is deployed, scaling is logarithmic — not linear.