LLM features in your product the way a senior engineer would build them
We integrate LLMs into your product the way you'd want your senior engineer to do it — typed, evaluated, monitored, and reversible. No prompt wrappers masquerading as AI features, no integrations that collapse under production load.
What we ship
Production LLM features — not demos, not Jupyter notebooks. Integrated into your existing codebase, evaluated against real data, monitored in production.
LLM features in your product
We add AI capabilities to your existing application — generation, classification, extraction, summarization — as modular, testable components that fit your architecture.
Custom AI APIs and SDKs
Internal or customer-facing AI endpoints — built on your model strategy, versioned, documented, and deployed behind your auth layer.
Structured outputs pipelines
Typed, validated output schemas with Instructor, function calling, or Anthropic tool use. LLMs that return structured data your app can actually consume — reliably.
Content generation at scale
Batch generation with caching, cost controls, and quality gates. For SaaS products that need LLM output at millions of operations per month without runaway costs.
Semantic search
Embedding-based search wired into your product — not a standalone vector DB experiment, but search that talks to your existing data layer and respects your access model.
Classification and extraction
Intent detection, entity extraction, document routing, label assignment — with typed schemas, confidence scores, and fallback handling when the model is uncertain.
Summarization at scale
Long-document summarization with chunking strategies tuned to your content type — legal, technical, support transcripts, financial — with output format validation and faithfulness checks.
Prompt engineering at production scale
Versioned prompt templates, A/B testing infrastructure, regression gates in CI. Prompt changes treated as code changes — reviewed, tested, and deployed with the same rigor.
The engineering stack
Tools chosen for your constraints — not because they were in the trending repo last week.
Model providers
OpenAI, Anthropic, Mistral. Open-source inference via Together AI, Modal, or AWS Bedrock. We route across providers based on cost, latency, and capability — no single-vendor lock-in.
Structured outputs
Function calling (OpenAI), JSON mode, Anthropic tool use, Instructor for Pydantic-validated schemas, Outlines for constrained open-source generation. We pick the right primitive — not the fashionable one.
Streaming
Server-Sent Events (SSE) for any stack, Vercel AI SDK for Next.js products. We wire streaming correctly — with backpressure handling, partial JSON parsing for structured outputs, and graceful error recovery.
Observability
LangSmith, Helicone, Langfuse, or custom logging pipelines. Every LLM call traced — prompt version, token count, latency, cost, output quality score. You know exactly what is happening in production.
Cost control
Prompt caching (cache_control on Anthropic), batching for async workloads, model routing — cheap for simple, expensive only when complexity warrants it. Smart truncation that preserves signal, not just character count.
Evaluation infrastructure
Custom eval sets, LLM-as-judge pipelines, regression gates wired into CI. Prompt changes don't ship without a benchmark run. Output quality is a first-class engineering metric, not a vibe check.
Model strategy — our opinionated take
We don't push a vendor. We match model to use case based on what actually matters in your context: context window, cost, latency, output format, and data residency.
Claude
Best for long-context reasoning, agents that need to follow complex multi-step instructions, and structured output tasks where JSON schema compliance matters. Prompt caching economics are excellent for stable system prompts. Also our pick for tasks requiring careful instruction-following.
GPT-4o / GPT-4 mini
Best for versatility and ecosystem integrations — if your team is already on Azure OpenAI, if you need multimodal inputs, or if you need a model that existing tooling and libraries support out of the box. GPT-4o mini for cost-sensitive high-volume paths.
Mistral / Llama
Best for cost-sensitive workloads and data sovereignty requirements. Self-hosted via Modal or Bedrock when no data can leave your VPC. Mistral for European data residency. Llama 3 via Together AI for the best open-source price-performance ratio at scale.
Fine-tuning
For style, format, and output consistency — not knowledge injection. Fine-tune when you need the model to reliably produce a specific output structure or match a domain tone at scale. Never fine-tune to memorize facts that change over time. Combine with RAG when you need both.
"In most production systems we route across multiple models. Cheap model for classification and triage, expensive model only when reasoning complexity warrants it. Model routing is cost optimization — not a compromise on quality."
Evaluation patterns
If you can't measure it, you can't ship it to production. Evals are not an afterthought — they are the engineering process.
Custom eval sets
We build task-specific eval sets from your real data — not synthetic benchmarks that don't reflect your distribution. Input-output pairs, edge cases, and known failure modes. The eval set is a deliverable, not an internal tool.
LLM-as-judge
We use LLM-as-judge for scalable eval — with documented scoring criteria, calibrated against human labels, and explicit caveats about where the judge model can be gamed or biased. Useful, not magic.
Regression testing in CI
Prompt changes, model version upgrades, and schema updates all run through the eval suite before merge. A regression gate blocks deployment if core quality metrics drop below threshold — same discipline as unit tests.
A/B testing in production
We wire prompt variants, model variants, and generation parameter changes through proper A/B infrastructure — with statistical significance checks, not "it seemed better on the staging env" rollouts.
Drift detection
LLM output quality degrades silently — model updates, prompt distribution shift, new user behaviors. We instrument drift detection on output quality metrics and alert before your users notice a regression.
"If you can't measure it, you can't ship it to prod."
We treat LLM quality as an engineering discipline — with baselines, regression gates, and production monitoring. Not a one-time vibe check before launch.
Cost optimization — the details
LLM costs are engineering decisions, not line items you accept. Every lever below has been applied in production workloads — with real numbers.
Prompt caching
Anthropic's cache_control enables caching stable system prompt prefixes. On workloads where a large system prompt is reused across thousands of requests — documentation, policy, persona — we consistently see 60-80% reduction in input token cost. Cache writes cost 25% more per token; cache hits cost 90% less. The math works as soon as cache hit rate exceeds ~23%.
Real example
8K-token system prompt, 50K requests/day. Without caching: ~$400/day input cost. With caching at 80% hit rate: ~$90/day. Payback on implementation: same day.Model routing
Not every request needs GPT-4o. We implement routing logic that classifies request complexity and sends simple, high-volume tasks (classification, formatting, extraction) to cheaper models while reserving expensive calls for tasks that genuinely require top-tier reasoning. Typical savings: 40-70% of inference cost on mixed workloads.
Batching
For non-latency-sensitive workloads — nightly processing, bulk enrichment, document analysis pipelines — we use OpenAI Batch API or Anthropic batch endpoints. 50% cost reduction at the model level for workloads that can tolerate up to 24-hour turnaround. Significant for high-volume asynchronous processing.
Smart truncation
When context windows are large but your relevant content is sparse, naive truncation destroys signal. We implement content-aware truncation — preserving the highest-signal chunks based on your specific task — and sliding window strategies that maintain coherence. We also instrument actual context utilization to find and eliminate padding.
How we work
Discovery to production — with eval gates at every stage. No handwaving, no "it works in the demo" surprises.
Discovery
We map your stack, your data, your latency constraints, and the exact user-facing task. We define success metrics before writing code.
- ✓ Task specification with success criteria
- ✓ Eval set design from real data
- ✓ Model and stack selection
- ✓ Cost model and latency budget
Prototype with eval set
We ship a working integration in 1-2 weeks — with the eval set instrumented from day one. We iterate on prompts, schemas, and model selection against measured benchmarks, not intuition.
- ✓ Working prototype wired into your stack
- ✓ Eval suite running on real data
- ✓ Prompt and schema iteration with metrics
- ✓ Cost and latency profiling
Productionize with monitoring
We harden the integration for production: error handling, fallbacks, streaming, CI regression gates, and observability. You can see every LLM call, cost, and quality metric from day one in production.
- ✓ Production-grade error handling & fallbacks
- ✓ CI eval regression gate
- ✓ Observability: traces, cost, quality
- ✓ Cost optimization (caching, routing)
Iterate
LLM features are not set-and-forget. We set up the infrastructure for your team to iterate — prompt versioning, A/B framework, drift alerts — and hand off with a runbook, not a black box.
- ✓ Prompt versioning & A/B framework
- ✓ Drift detection & alerting
- ✓ Engineering runbook & handoff
- ✓ Model upgrade path documentation
Things we won't do
Being opinionated about what not to build is part of the service. We'd rather tell you upfront than charge you to build something wrong.
Chase the newest model for marketing
Upgrading to a new model release without running your eval suite is a rollback waiting to happen. We benchmark before we recommend any model change. "GPT-5 just dropped" is not a reason to migrate.
Gold-plate RAG when prompting works
RAG is the right architecture for many problems — not all of them. If your use case is solved with a well-structured prompt and a 32K context window, we'll tell you that, not sell you a vector database you don't need. See our RAG systems page for when RAG is the right call.
Integrate without evals
An LLM integration without an eval suite is a feature you can't safely iterate on. Every integration we ship includes an eval set and a regression gate. No exceptions. If you already have an integration with no evals, we'll help you add them as a first step.
Hide behind "AI magic"
Every decision — model choice, prompt structure, schema design, eval criteria — has a reason. We document the reasoning. Your engineering team should be able to understand, audit, and improve the system after we hand it off. Not just run it and hope.
Related reading
Technical content for engineering teams evaluating LLM integration approaches.
RAG Systems Engineering
When your use case needs retrieval over a private corpus — chunking strategy, hybrid search, reranking, and RAGAS-evaluated deployments.
Explore RAG systems → ServiceAI Agents
When LLM features need to take actions — tool use, multi-step reasoning, autonomous workflows. How we build agents that are reliable, auditable, and safe to ship.
Explore AI agents → Technical GuideUnderstanding RAG architecture
A technical guide to embeddings, vector stores, retrieval strategies, and evaluation — written for engineers, not executives.
Read the guide → ServiceAI Audit
Not sure which features to build or how to prioritize? We audit your product and data to identify the highest-ROI LLM integration opportunities before any engineering starts.
Start with an audit →Technical FAQ
cache_control economics; GPT-4o for versatility, multimodal inputs, and teams already on Azure OpenAI; Mistral or Llama when data sovereignty matters or when open-source inference on Modal or Bedrock cuts your cost significantly. In most production systems we route across multiple models — cheap for simple tasks, expensive for complex reasoning. The model strategy document we deliver at the end of discovery is vendor-agnostic and explains every tradeoff.
cache_control delivers 60-80% cost reduction on stable system prompts with a 23% cache hit rate breakeven); model routing (40-70% savings by sending simple tasks to cheap models and reserving expensive models for complex reasoning); batching (50% model-level discount via OpenAI Batch API or Anthropic batch endpoints for async workloads); and smart context truncation that preserves signal rather than just truncating by character count. We instrument cost per feature from day one — you always know what each capability costs to run at scale.
Ship LLM features that hold up in production
Book a technical call. We will assess your use case, your stack, and your data — and give you a straight answer on what to build, which model to use, and what it will realistically cost to run. No pitch deck, no commitment required.
Or explore related services: RAG Systems · AI Agents · AI Audit · Contact