WORKFLOX — San Francisco
AI Agent Engineering for San Francisco Startups and Scale-Ups
Most Bay Area teams we talk to already have a working agent prototype. The gap is everything after that: eval coverage, retry and fallback behavior, token cost that doesn't scale linearly with usage, tracing you can actually debug at 2am, and the long tail of tool-call failures that only appear under real traffic. WORKFLOX works as an extension of San Francisco engineering teams — we take the production hardening and the unglamorous surface area so your in-house engineers stay on the part of the product that is actually differentiated.
Why WORKFLOX
Why Businesses in San Francisco Choose WORKFLOX
We Extend Your Team, We Don't Replace It
Your engineers own architecture and product direction. We take agent tooling, eval harnesses, connector work, and the integration backlog that never reaches the top of your sprint.
Evals Before Features
We build a graded test set against your real traffic before touching the agent loop. Every change ships with a measured delta, so 'it feels better' stops being the acceptance criterion.
Cost and Latency as Design Constraints
Model routing, prompt caching, structured outputs instead of parse-and-pray, and selective retrieval. We treat p95 latency and cost-per-resolved-task as first-class metrics from day one.
Code You'd Pass in Review
Typed interfaces, deterministic tests around non-deterministic components, real CI, and documented failure modes. You get the repo, the traces, and the runbook — no black boxes and no lock-in.
San Francisco Market
AI Agents Development Landscape in San Francisco
San Francisco is the densest AI engineering market on earth, and that creates an unusual constraint rather than an advantage: senior AI talent is expensive, slow to hire, and immediately absorbed by core product work. Venture-backed teams here routinely reach a working agent demo in a weekend and then spend two quarters getting it to a state where they'd let a customer touch it. The bottleneck in this market is almost never the idea or the model choice — it's eval infrastructure, integration surface, and the engineering hours to grind reliability from 80% to the high nineties on the tasks that matter. The Bay Area's concentration of foundation labs also means local buyers move fast on new model capabilities, so anything built here has to assume the underlying model will be swapped within months. We build model-agnostic agent layers for that reason. The other pressure is runway: post-Series A teams need shipped, revenue-adjacent agent features on a defined budget, not an open-ended research program.
Our Services
Services We Offer in San Francisco
Technology
Our Stack
We use proven, production-grade technologies — selected for performance, security, and maintainability.
FAQ
Frequently Asked Questions
We already have strong engineers. Why bring in an external team?
Because agent reliability work is high-effort and low-status, and it competes badly against roadmap work internally. We take eval harnesses, connector builds, retry/fallback logic, observability wiring, and the integration backlog. Your team keeps the core. Several of our engagements start as a two-week scoped chunk specifically so you can evaluate the code before committing further.
What does an engagement cost and how is it structured?
A focused agent build or a scoped production-hardening engagement runs $6,500-16,000 fixed price, typically 3-6 weeks. Multi-agent systems, complex tool ecosystems, or a full agent platform layer run $21,500-65,000. US agencies bill $150-350/hour for equivalent seniority; we run 40-60% under that on fixed scope.
How do you handle evaluation and regression testing for non-deterministic systems?
We build a graded dataset from your real traffic and failure logs, run it in CI on every change, and track per-task pass rates plus cost and latency distributions. LLM-as-judge where it's defensible, deterministic assertions where it isn't, and human-labeled golden sets for anything customer-facing. You get the harness, not just the results.
Do we own the code, and what happens when the engagement ends?
You own everything — repo, prompts, eval sets, infrastructure-as-code, the lot. We work in your GitHub org on your cloud accounts where possible. Every engagement includes 60 days of post-launch bug-fix support, and we hand over a runbook covering known failure modes rather than a slide deck.
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