San Francisco teams operate with high technical bars and low patience for hand-wavy delivery. In-house engineers are costly; contractors rotate; AI features get demoed before retrieval and evals exist. The market rewards instrumentation, billing, and admin surfaces early — not slideware. Many founders run React or Next.js and need capacity that respects code review culture and enterprise security questionnaires. Boutique agencies struggle to keep senior talent on accounts. Jythu positions as a product engineering partner — LLM integration with cost caps, SaaS rebuilds with Stripe, and staff-aug pods on the same Zoom weekly.
Jythu wires large language models into products you already run — RAG over your docs and databases, tool-calling agents with guardrails, eval harnesses, and cost controls finance can read. Not a chat widget stapled on a homepage. We integrate OpenAI, Anthropic, or open weights behind your VPC with citations, rate limits, and observability baked in. For San Francisco teams, that usually means fewer contractor handoffs and a partner who will cut scope when the calendar is real.
Bay Area — venture-backed SaaS, AI tooling, and infra companies on Pacific Time (PT). We tailor AI & LLM integration around local collaboration habits — local proof section, city case study/testimonial, timezone/response-time note.
AI & LLM Integration — nearby services
Built for this market — not a generic offshore brief
Why teams in San Francisco hire Jythu
Senior ICs who have shipped SaaS and AI in production — we speak PR review, feature flags, and unit economics on Pacific mornings, not buzzwords in a pitch deck.
Senior builders, written scope
Two-week sprints, staging on every PR, and deliverables your team can own — not a black-box ticket queue.
US business-hours overlap
Core standups land in your workday with async PRs ready when you log on.
In-product assistant (NDA)
Support volume and docs sprawl pushed a product team to put answers inside the app. Answers had to cite sources, never hallucinate pricing, and stay under a weekly cost cap.
Result: Hybrid RAG over CMS and product DB with tool calls for account actions behind confirmation. Material share of tier-1 tickets deflected; cost per 1k queries became predictable after caching.
Discover → Design → Build → Launch
Discover
Goals, constraints, and success metrics in a focused discovery pass.
Design
Figma flows approved before TypeScript starts — fewer surprises later.
Build
Two-week sprints with staging deploys you can click every cycle.
Launch
Cutover checklist, monitoring, and a 30-day warranty on fixed projects.
Questions teams ask before kicking off
Can you integrate LLM features into our existing SF codebase?
That is our default AI scope — RAG, tool calling, evals, and observability in your repo, with keys and data handling configured to your policies.
How do you handle Bay Area security reviews?
We work in your cloud accounts, follow your access checklist, and document data flows for vendor reviews — without claiming to be your lawyer.
What does staff augmentation look like from India?
Named engineers, overlap on PT mornings, sprint demos with written decisions. You retain repo and infra ownership.
How do you keep AI answers grounded in our data?
We start with a discovery call, then a written in/out list for AI & LLM integration. Estimates follow constraints — timeline, stack, and who owns the repo — not a one-size quote calculator.
5.0 · Rated by 50+ clients
Ready to start a project?
The first discovery call is on us. Bring constraints — we will bring a timeline and a cut list.