"Add AI to the product" is on the roadmap. The proof of concept worked in three weeks. Then production realities arrive — latency budgets, cost runaway, streaming UX, fallback paths for when the model is slow or wrong. The gap between demo and production is where most LLM features die, and it's wider in fintech than anywhere else.
Five specific failure modes we see when LLM features get shipped into existing SaaS products. The product was already working — the AI feature is either making it better, or quietly breaking the trust users had in it.
The product responds to other actions in 200ms. The LLM feature takes 4 seconds. Users assume the app is broken, click again, generate duplicate requests, and the support team's queue fills up by lunch. Without streaming, latency budgets, or 'we're thinking' UX patterns, the new feature degrades the perceived quality of the product around it.
In dev, the feature costs $0.01 per request. The team estimates monthly costs of a few hundred dollars. Then production traffic arrives: users who hammer the feature, prompt-injection attempts, edge cases that trigger longer model outputs. The first invoice from your model provider is 40× the estimate.
Your model provider has a 99.9% SLA — which means roughly 8 hours of degraded service per year. When the model is timing out or returning errors, what does your feature do? If the answer is 'show an error page that scares the user,' the feature is more fragile than the product it lives in. Production features need graceful degradation paths.
The team built an eval set of 50 examples that captures the demo. Production receives 50,000 requests a week, with edge cases the eval set never anticipated — sarcasm, multilingual inputs, prompt injection, malformed structured outputs. The eval passes; production fails. Without continuous eval expansion from real production traces, the gap between 'eval green' and 'users happy' is invisible.
The model is 60% confident. The feature shows the result as if it were 100% confident. Users build trust on confident outputs, then lose trust when an obviously-wrong answer arrives with the same confident tone. Production LLM UX needs to communicate when the model is sure, when it's not, and when the user should sanity-check.
Production LLM features with latency budgets, cost controls, fallback paths, and eval coverage that grows with real production traffic — all wired into your existing SaaS product.
Every feature gets a hard latency budget — time-to-first-token, time-to-completion, perceived latency to the user. We design the prompt strategy, model choice, and streaming behavior around that budget. Users see thinking states, partial responses, or skeleton loaders — never a blank screen while the model decides what to say.
Per-request token caps, per-user daily limits, model-tier routing (cheap model for simple cases, expensive model when justified), and circuit breakers when costs spike unexpectedly. Your first production month doesn't arrive with a surprise invoice. Your CFO doesn't get a surprise meeting on the budget line item.
Model timing out? Cached response, simpler model, or rule-based fallback — whichever fits the feature. Provider returning errors? Automatic failover to a backup provider if you have one, or a clear 'we're having trouble, try again in a moment' state that doesn't break user trust. The feature degrades gracefully; it doesn't crash.
Initial eval set captures the demo scenarios. Then production traces — sampled and anonymized — flow back into the eval set continuously. Edge cases, prompt injection attempts, multilingual inputs, sarcasm, malformed outputs: all get added. The eval set evolves with real user behavior, not just the team's imagination.
The model is sure? Show the answer confidently. The model is uncertain? Show alternatives, ask for clarification, or surface 'we're not sure — here's why' messaging. We wire confidence signals into the UI so users build calibrated trust — not the confident-but-wrong trust that ends in support tickets.
Concrete deliverables of an LLM feature engagement. Everything ships into your existing product, in your stack, under your control.
Integrated into your existing product, behind a feature flag, ready for staged rollout.
Prompts versioned in your repo, not in a model provider's UI. Changes go through code review.
Real-time dashboards for P95 latency, token costs, cache hit rates, error rates. Integrated with your existing observability stack.
Cached responses, simpler-model routing, rule-based fallbacks, or graceful error states
Initial eval set plus the infrastructure to grow it from production traces. CI integration blocks regressions.
Per-user, per-feature, per-model-tier limits. Circuit breakers on cost anomalies.
Two sessions with your team to walk through the feature, the evals, prompt management, and how to iterate safely.
Direct Slack access for the first month after handoff, with response within one business day.
Three to ten weeks, broken into four phases. Predictable rhythm, transparent progress, the feature lives behind a flag in your production environment from week two.
Week 1. We work with you to define what the feature does, where it lives in the product, what the latency budget is, what the cost ceiling is, and what the fallback behavior looks like. We design the UX states — loading, streaming, error, low-confidence — alongside the prompt strategy. You get a written scope and a Figma prototype by end of week 1.
Weeks 2–6 typically. The feature ships to your production environment behind a feature flag from week 2. Your engineers can review PRs, run the feature in staging, and watch it work on real data — none of which requires a production rollout. Weekly demos, code in your repo.
Weeks 4–8 typically. Eval suite built and integrated into your CI. Latency and cost monitoring wired into your observability stack. Fallback paths tested by deliberately breaking the model provider in staging. Rate limits and circuit breakers configured. By the end of phase, the feature is ready for staged rollout to real users.
Final week of engagement plus 30 days after. Documentation finalized, two handoff training sessions with your team, direct Slack access for 30 days post-launch. After day 30, optional retainer available. No lock-in, no platform fees, no surprise renewals.
Things buyers commonly ask about LLM feature engagements. If your question isn't here, the call is the easiest way to get an answer.
Yes. We work in your repo, in your language, with your existing patterns. If your product is TypeScript on AWS Lambda, that's what we ship in. If it's Ruby on Rails on Heroku, same. We're not bringing a proprietary BlueSoft framework — we're adding the LLM feature in the way your team would have built it if you had the bandwidth.
Depends on your latency budget, cost ceiling, accuracy requirements, and compliance constraints. We've shipped on OpenAI, Anthropic, Bedrock, Vertex, and self-hosted (Llama, Mistral). We'll recommend based on your specifics, but the choice is yours — and so is the bill. We make the integration provider-agnostic where possible so the choice isn't locked in forever.
It's the hard ceiling on how long the feature can take before the user thinks the product is broken. For a chat interface, that's usually time-to-first-token under 1.5 seconds. For a background enrichment task, it could be 10 seconds. The latency budget drives every other decision — prompt length, model choice, caching strategy, streaming vs. blocking. Without it, you ship a feature that works in dev and feels broken in production.
Depends on your compliance constraints. If you can send raw data to the model provider with a BAA or DPA in place, we do that with appropriate logging boundaries. If you can't, we use PII redaction before the model call and re-hydration after — for example, replacing customer names with tokens that we map back in post-processing. For high-sensitivity data, we use self-hosted models or providers with zero-retention agreements. We work within your existing compliance posture.
That's the whole point. Prompts live in your repo, versioned with the rest of your code. The eval suite catches regressions when prompts change — your team can iterate confidently because the CI tells them if they broke something. We walk through the prompt management workflow in the handoff training so your team owns the iteration loop.
First call is 30 minutes. You describe the feature you're trying to ship, where it lives in your product, and what's in the way. We ask technical questions about your stack, your latency requirements, and your compliance posture. By the end of the call, we'll both know whether this is something we should build together.