Custom software development
Production AI-native systems: agent platforms, copilots, vertical AI SaaS, evaluation infrastructure, model-serving fleets. TypeScript, Python, Rust where latency matters, Postgres, pgvector, on AWS, GCP, or Modal.
Locations · San Francisco, CA
Software Development Company in San Francisco — Nearshore Engineering Teams
YuSMP Group builds custom software, MVPs, and foundation-model-native AI integrations for San Francisco AI infra startups, YC alumni, and Series-A to Series-C founders through senior EU nearshore teams. Production-grade Claude 4.6, GPT-4o, o3 and Llama 4 systems, eval-driven iteration, dedicated teams from 12,000 EUR per month, contracts via German GmbH. The engineering capacity you cannot poach away from Anthropic and OpenAI.
San Francisco in 2026 is the global capital of the foundation-model and applied-AI economy. Anthropic, OpenAI, xAI, Mistral SF office, Scale, Databricks, and the long tail of Y Combinator W25, S25, W26, and S26 AI-native startups have concentrated more frontier AI engineering talent inside a 7-by-7-mile city than any other geography on the planet. The flip side is the most extreme engineering labour market in history: senior ML engineer comp at Series-B AI infra companies routinely clears 500k USD fully loaded, with anchor packages at the frontier labs above 1M USD. Even at YC seed stage, founders compete for ML engineers against employers who can offer Anthropic or OpenAI equity. Time-to-hire for a senior ML engineer with production foundation-model experience is 130+ days. Time-to-hire for a senior full-stack engineer who can build the auth, billing, dashboards, and customer-facing surface area around the AI core is 95 days. We are the asymmetric answer: senior EU engineers who have shipped Claude 4.6 and GPT-4o into production for European AI startups since these models existed, available in three to five weeks, at one-quarter of fully-loaded SF comp, contracted through a German GmbH with clean Delaware-compatible IP assignment.
Six software services we deliver for San Francisco clients
MVP development
From 25,000 EUR and 6 to 10 weeks to a fundable demo. YC batch founders shipping in time for demo day, partner meetings, or the first seed conversations. AI-native architecture from day one.
Dedicated development teams
2 to 12-engineer squads with at least one ML-fluent engineer per pod. Embedded in your Slack, Linear, GitHub. Same team for 12+ months, no rotations. From 12,000 EUR per month per pair.
Foundation-model & AI integration
Claude 4.6 Sonnet and Opus, GPT-4o, o3, Llama 4 8B/70B/405B integrations. Agent harnesses (LangGraph, custom), RAG (pgvector, Pinecone, Weaviate), fine-tuning, eval-driven iteration on Braintrust or LangSmith, self-hosted inference on H100/H200/Trainium.
Cloud & DevOps
AWS us-west-2, GCP us-west1, Modal, Together, Fireworks, multi-cloud GPU orchestration. Terraform, EKS, GPU autoscaling, cost-aware model routing. FinOps reviews that consistently cut LLM and GPU bills 30 to 50 percent.
Compliance & AI governance
SOC 2 Type I/II, CCPA, GDPR for European users, EU AI Act readiness for SF startups selling into the EU. Model cards, eval reports, sub-processor schedules, DPAs that handle the LLM-provider chain correctly.
Why nearshore from the EU vs hiring locally in San Francisco
SF Bay Area senior engineering comp in 2026 is unprecedented. Senior ML engineers with production foundation-model experience earn 320k to 480k USD base at Series-B AI infra companies, 500k to 800k fully loaded with equity, benefits, payroll taxes, and an allocated share of FiDi or Mission office. Senior backend or full-stack at non-AI Series-B startups earn 240k to 320k base, 340k to 460k fully loaded. The frontier labs have set a new comp ceiling; everyone hiring AI talent has to clear a multiple of it to even open a conversation. Time-to-hire for a senior ML engineer in SF is 130+ days because that engineer has offers from Anthropic, OpenAI, xAI, Mistral, and three Series-A's at every interview. Voluntary attrition for senior engineers at SF AI-adjacent companies ran at 24 percent in 2025 according to Carta — you re-hire the same seat every four years.
Our senior EU engineers bill 65 to 95 EUR per hour all-in. A four-person dedicated team with at least one ML-fluent engineer is 14,000 to 24,000 EUR per month (around 180k to 305k USD per year), versus 1.4M to 2.4M USD per year for the same four engineers in SF. Time-to-team is three to five weeks. The engineers we put on SF AI accounts have shipped Claude 4.6, GPT-4o, and Llama 4 into production for European AI startups since these models existed; they know what an eval harness has to look like, why Anthropic rate-limits matter for cost modelling, and how to architect an agent that does not loop. Attrition on our side ran at 6 percent in 2025.
Three typical San Francisco client scenarios
YC S26 AI-native founder, SoMa
Solo technical founder, vertical AI agent for an enterprise workflow, demo day in 9 weeks. Founder is doing the agent core and customer development; cannot also build auth, billing, dashboards, and the eval harness. We embed 1 ML engineer + 1 full-stack engineer on Claude 4.6 + Next.js + Postgres + pgvector + Stripe. Demo-day-ready in 8 weeks at 17,500 EUR for the engagement.
Series-B AI infra company, FiDi
90-engineer team building inference and observability infrastructure. ML and platform teams are senior and hard-to-hire; product and customer-facing surface area (dashboards, billing, admin, marketing-site app shell) is the bottleneck. We add a 5-engineer product squad alongside their SF core. From 22,000 EUR/month, 9 AM PT joint standup window via the half-shifted EU half-team.
Series-A applied AI in Mission
Vertical AI for legal, post-Series-A, 25 engineers. Need to ship multi-model routing (Claude 4.6 Opus for hard reasoning, GPT-4o for cheap drafts, Llama 4 70B self-hosted for sensitive customer data) with a cost-aware router and a customer-facing eval/feedback loop. We add 3 ML engineers + 1 DevOps for 12 months. From 17,000 EUR/month.
Stack we run for SF clients
TypeScript / Next.js
Python / FastAPI
Rust (low-latency)
Postgres / pgvector
Pinecone / Weaviate
Claude 4.6 (Sonnet, Opus)
GPT-4o / o3
Llama 4 8B/70B/405B
LangGraph
Braintrust / LangSmith
Modal / Together / Fireworks
AWS us-west-2
Trn2 / Inf2
H100 / H200 clusters
Terraform
EKS
Stripe / Orb
WorkOS / Clerk
SOC 2 (Drata)
EU AI Act readiness
Legal entity, contracting, and IP
YuSMP Group contracts with San Francisco clients through YuSMP GmbH, registered in Berlin, Germany. Master Services Agreements are typically governed by Delaware law with venue in San Francisco County for SF-headquartered clients; NDA and IP assignment match a Delaware C-corp's founder paper so investor and acquirer diligence is clean. We sign DPAs aligned to GDPR and CCPA for SaaS handling EU and California user data; the LLM-provider sub-processor chain (Anthropic, OpenAI, AWS Bedrock, GCP Vertex, Together, Fireworks, Modal) is handled in our standard sub-processor schedule and updated as you add or remove providers. All code, infrastructure-as-code, model artefacts, fine-tuned weights, eval datasets, prompt libraries, and agent traces transfer to the client as work-for-hire on invoice payment; no background-IP carve-outs except clearly disclosed open-source dependencies, delivered as a bill of materials per release.
Pricing anchors
MVP from 25,000 EUR
6 to 10-week fixed-price MVP for YC-batch and pre-seed SF founders. AI-native architecture from day one, eval harness included, demo-day-ready package.
Dedicated team from 12,000 EUR/mo
2-engineer pair on a 12-month engagement, full-time, same team. At least one ML-fluent engineer per pair on AI engagements. Scales to 12 engineers per squad.
Fractional CTO from 4,500 EUR/mo
For SF founders between CTO hires or running engineering hiring, architecture, vendor selection (Claude vs GPT vs Llama), and SOC 2 readiness in parallel. See the fractional CTO page.
How a San Francisco engagement starts
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01
Discovery call
30-minute Zoom with the founder or CTO. We map the work, the model stack (Claude / GPT / Llama / fine-tunes), the eval strategy, and the demo-day or fundraise countdown.
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02
Scoped proposal
Within 5 business days: written proposal with team composition (including ML coverage), deliverables, monthly pricing, eval harness plan, and a 3-month minimum term. MSA and SOW drafts attached.
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03
Kickoff & embedding
Week 1: engineers in your Slack, Linear, GitHub. EU team half-shifts to align with PT mornings. Engagement lead meets you on Zoom daily for the first sprint, in SF in person at quarter end if scope justifies.
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04
Operating cadence
Daily standup at 9:00 to 10:00 AM PT (overlap window), weekly sprint review, bi-weekly retro, weekly eval-result review, monthly written status report.
Frequently asked questions from San Francisco founders
Do you have experience with Anthropic Claude, OpenAI, and open-source LLM stacks?
Yes — LLM integration is the single largest workload across our SF book in 2026. We ship production systems on Claude 4.6 (Sonnet and Opus), GPT-4o, o3, and Llama 4 (8B, 70B, 405B variants where appropriate). We run RAG pipelines (pgvector, Pinecone, Weaviate), agent frameworks (LangGraph, our own internal harness, OpenAI Assistants where the customer requires it), eval-driven iteration (Braintrust, LangSmith, custom golden-set harnesses), and self-hosted inference on H100/H200 clusters via Modal, Together, Fireworks, or direct AWS Trn2/Inf2. We sign DPAs that handle the Anthropic and OpenAI sub-processor chain correctly for enterprise customers.
Are you a fit for a YC-stage founder who needs to ship a fundable AI demo fast?
It is one of the most common reasons SF founders hire us. Typical profile: YC W26 or S26 batch, technical co-founder is the only engineer, demo day or partner-meeting deadline in 6 to 10 weeks, the founder is doing recruiting and sales and cannot personally build out the auth, billing, dashboards, eval harness, and the second model integration. We embed one ML engineer plus one full-stack engineer at 8,000 to 11,000 EUR per month per pair, ship the unsexy supporting layer of the product, and let the founder stay on the actual moat. Three-month minimum, month-to-month thereafter.
How does this compare with hiring senior AI engineers in SF in 2026?
SF in 2026 has the most expensive senior engineering market in history. Senior ML engineer comp at a Series-B AI infra company runs 320k to 480k USD base + equity, fully loaded 500k to 800k USD per year. Senior backend or full-stack at a non-AI Series-B runs 240k to 320k base, 340k to 460k fully loaded. Time-to-hire for a senior ML engineer with foundation-model production experience runs 130+ days because they have offers from Anthropic, OpenAI, xAI, Mistral, and three Series-A's at every interview. Our nearshore senior engineers cost 155k to 280k USD per year for a four-person team, available in three to five weeks.
What is the time-zone overlap with an SF team?
Pacific Time is the toughest US overlap with CET; the practical window is mornings on the SF side. Our engineers wrap their day around 6:00 to 7:00 PM CET, which is 9:00 to 10:00 AM PT — about 90 minutes to 3 hours of solid joint working time per day depending on the engineer's flexibility. We staff SF accounts deliberately: half the team shifts to a 11:00–20:00 CET workday so the overlap stretches to 11 AM to 1 PM PT, a full 4-hour synchronous window for standups, design reviews, and pairing. Async-first communication and well-structured Loom-plus-Linear handoffs do the rest of the work.
Will you sign Delaware-state IP assignment and contract under US law?
We contract through YuSMP GmbH (Berlin, Germany) for EU and US clients. Master Services Agreements are typically governed by Delaware law with venue in San Francisco County for SF-based clients; NDA and IP assignment match Delaware C-corp founder paper so investor and acquirer diligence is clean. We sign DPAs aligned to GDPR and CCPA for SaaS handling EU and California user data, and the LLM-provider DPA stack (Anthropic, OpenAI, AWS Bedrock) is handled in our standard sub-processor schedule. All code, infrastructure-as-code, model artefacts, fine-tuned weights, eval datasets, and documentation transfer to you as work-for-hire on invoice payment.