LLM Integration · San Francisco, CA

LLM integration in San Francisco.

San Francisco does not need convincing that language models work. The problem here is different: prototypes that demo well but cannot be charged for, AI features with no evaluation behind them, and inference bills growing faster than the revenue they support. LLM integration for SF products means closing the gap between a promising demo and a feature that holds up under paying customers.

We embed and harden LLM features inside your product: prompt architecture, model benchmarking on your data, structured output, streaming UX, caching, fallbacks, and the evaluation harness that makes iteration safe.

How we work

Tell us what you are shipping and what is in the way.

The technical reality of LLM features in SF products.

The common SF failure mode is not a bad idea; it is a prototype promoted to production by enthusiasm. No eval set, so every prompt tweak is a gamble. No cost instrumentation, so the first real invoice is a surprise. No fallback, so a vendor incident becomes your incident. The fix is not a rewrite. It is wrapping the feature in the boring infrastructure that demos skip: evaluation, metering, fallbacks, and logs.

Model choice deserves less loyalty and more measurement. We benchmark Claude, GPT, and the strongest open-weight options against a labeled sample of your real traffic, then route by difficulty where volume justifies it. Teams are often surprised how much of their traffic a model at a tenth of the price handles at equal quality.

Competitive pressure here makes latency a product feature. Streaming, prompt caching, and small-model routing are the difference between a feature that feels instant and one that feels like a loading screen. We design to a p95 budget per interaction, not an average.

Enterprise sales adds its own requirements. The moment a mid-market customer's security team reviews your product, your model vendors become subprocessors, your prompt logs become an audit topic, and 'we don't train on your data' needs paper behind it. Building those answers into the integration up front is cheaper than retrofitting them during a stalled deal.

What we build for San Francisco teams.

Six engagement shapes we scope most often for SaaS, fintech, and developer-tool products.

Prototype-to-production hardening

Your existing AI feature wrapped in evaluation, fallbacks, abuse handling, cost metering, and observability, shipped behind a flag in two to four weeks.

Evaluation harnesses

Labeled eval sets from real user traffic, regression runs on every prompt or model change, and quality dashboards so product decisions are measured, not argued.

Multi-model routing

An internal model interface your code owns, difficulty-based routing across Claude, GPT, and open-weight options, and a tested fallback path for vendor incidents.

Streaming and latency engineering

Token streaming, prompt caching, skeleton states, and cancelable operations designed against an explicit p95 budget per feature.

Enterprise-readiness paperwork

Zero-retention vendor configuration, subprocessor documentation, tenant-attributed logging, and the data-flow diagram your customers' security reviews keep asking for.

Cost instrumentation

Per-feature and per-tenant spend tracking wired into your analytics, with caching and routing tuned until unit economics survive your pricing page.

Built for how San Francisco ships.

San Francisco companies are early adopters across SaaS, fintech, biotech, and developer tools, and the integration conversations here start further along than anywhere else. The question is rarely whether to use a language model. It is which model, behind what interface, measured by what eval, at what unit cost. We meet teams at that altitude.

The deployment mix leans pragmatic: enterprise API endpoints with zero-retention terms for most products, cloud-tenancy deployments (Bedrock, Azure OpenAI) where fintech compliance or enterprise contracts require it, and open-weight models where margins or data policy demand them.

We work with SF teams remotely, with demos and pairing sessions on video in Pacific hours. Typical engagements run two to six weeks from kickoff to a hardened feature in production.

Questions from San Francisco buyers.

We built an AI prototype in a weekend and customers want it. What does production-hardening involve?: The gap between a working demo and a feature you can charge for is usually five things. An evaluation set built from real user inputs, so prompt changes are measured instead of vibed. Timeout, retry, and fallback behavior, because the model API will have a bad day during your biggest customer's onboarding. Cost instrumentation per request, because the demo's economics rarely survive real traffic. Abuse and injection handling on any user-supplied text. And observability: logged prompts, outputs, latencies, and token counts queryable when something looks off. We typically harden a weekend prototype into a billable feature in two to four weeks without rewriting it.
Should we build against one model or several? Vendor lock-in worries us.: Build against an internal interface, not a vendor SDK, and the lock-in question mostly disappears. We structure integrations so prompts, tool definitions, and output parsing sit behind one boundary your code owns. Swapping Claude for GPT, or adding an open-weight fallback, becomes a configuration change plus an eval run rather than a refactor. Whether you should actively route across vendors is a separate question: routing earns its complexity when you have high volume with mixed difficulty, or a hard uptime requirement that justifies a second provider on standby. For most Series A products, one primary vendor with a tested fallback is the right amount of architecture.
What latency can users actually expect from an LLM feature, and how do we design for it?: Time-to-first-token on current frontier models typically runs 300 to 900 milliseconds, with full responses taking 2 to 15 seconds depending on output length. That is too slow to hide, so the UX has to acknowledge it: stream tokens as they arrive, render skeleton states, and make long operations cancelable. On the engineering side, we cut perceived latency with prompt caching (which can drop time-to-first-token sharply on repeated context), smaller models for latency-sensitive paths, and precomputation where the input is predictable. We set a p95 latency budget per feature during scoping and test against it, because average latency hides the experiences that lose users.
Our customers' security teams ask about our AI vendors during SOC 2 review. What do they need to see?: Enterprise buyers reviewing your SOC 2 will ask three things about an LLM integration: where customer data goes, whether the model vendor trains on it, and how access is logged. We set the integration up so the answers are clean. Model vendors are documented as subprocessors with zero-retention enterprise terms, customer data in prompts is minimized and never used for training, and every request is logged with tenant attribution to immutable storage. You get a one-page data-flow diagram and a vendor-security summary you can drop into questionnaire responses. That paperwork, boring as it is, has unblocked more enterprise deals than any model upgrade.

Ready to scope your integration?

Tell us what the feature does, where the prototype stands, and what is blocking production. We reply within one business day with a rough scope and a fixed price range.

LLM integration in San Francisco.

The technical reality of LLM features in SF products.

What we build for San Francisco teams.

Six engagement shapes we scope most often for SaaS, fintech, and developer-tool products.

Prototype-to-production hardening

Your existing AI feature wrapped in evaluation, fallbacks, abuse handling, cost metering, and observability, shipped behind a flag in two to four weeks.

Evaluation harnesses

Labeled eval sets from real user traffic, regression runs on every prompt or model change, and quality dashboards so product decisions are measured, not argued.

Multi-model routing

An internal model interface your code owns, difficulty-based routing across Claude, GPT, and open-weight options, and a tested fallback path for vendor incidents.

Streaming and latency engineering

Token streaming, prompt caching, skeleton states, and cancelable operations designed against an explicit p95 budget per feature.

Enterprise-readiness paperwork

Zero-retention vendor configuration, subprocessor documentation, tenant-attributed logging, and the data-flow diagram your customers' security reviews keep asking for.

Cost instrumentation

Per-feature and per-tenant spend tracking wired into your analytics, with caching and routing tuned until unit economics survive your pricing page.

Built for how San Francisco ships.

We work with SF teams remotely, with demos and pairing sessions on video in Pacific hours. Typical engagements run two to six weeks from kickoff to a hardened feature in production.

Questions from San Francisco buyers.

We built an AI prototype in a weekend and customers want it. What does production-hardening involve?

The gap between a working demo and a feature you can charge for is usually five things. An evaluation set built from real user inputs, so prompt changes are measured instead of vibed. Timeout, retry, and fallback behavior, because the model API will have a bad day during your biggest customer's onboarding. Cost instrumentation per request, because the demo's economics rarely survive real traffic. Abuse and injection handling on any user-supplied text. And observability: logged prompts, outputs, latencies, and token counts queryable when something looks off. We typically harden a weekend prototype into a billable feature in two to four weeks without rewriting it.

Should we build against one model or several? Vendor lock-in worries us.

Build against an internal interface, not a vendor SDK, and the lock-in question mostly disappears. We structure integrations so prompts, tool definitions, and output parsing sit behind one boundary your code owns. Swapping Claude for GPT, or adding an open-weight fallback, becomes a configuration change plus an eval run rather than a refactor. Whether you should actively route across vendors is a separate question: routing earns its complexity when you have high volume with mixed difficulty, or a hard uptime requirement that justifies a second provider on standby. For most Series A products, one primary vendor with a tested fallback is the right amount of architecture.

What latency can users actually expect from an LLM feature, and how do we design for it?

Time-to-first-token on current frontier models typically runs 300 to 900 milliseconds, with full responses taking 2 to 15 seconds depending on output length. That is too slow to hide, so the UX has to acknowledge it: stream tokens as they arrive, render skeleton states, and make long operations cancelable. On the engineering side, we cut perceived latency with prompt caching (which can drop time-to-first-token sharply on repeated context), smaller models for latency-sensitive paths, and precomputation where the input is predictable. We set a p95 latency budget per feature during scoping and test against it, because average latency hides the experiences that lose users.

Our customers' security teams ask about our AI vendors during SOC 2 review. What do they need to see?

Enterprise buyers reviewing your SOC 2 will ask three things about an LLM integration: where customer data goes, whether the model vendor trains on it, and how access is logged. We set the integration up so the answers are clean. Model vendors are documented as subprocessors with zero-retention enterprise terms, customer data in prompts is minimized and never used for training, and every request is logged with tenant attribution to immutable storage. You get a one-page data-flow diagram and a vendor-security summary you can drop into questionnaire responses. That paperwork, boring as it is, has unblocked more enterprise deals than any model upgrade.

Ready to scope your integration?

Tell us what the feature does, where the prototype stands, and what is blocking production. We reply within one business day with a rough scope and a fixed price range.