( AI )
LLM integration that actually ships
We connect large language models to your product — not as a demo, but as a production feature. From architecture to deployment, every decision is made for reliability, cost, and speed.
Whether you need a RAG pipeline, fine-tuned model, or multi-agent system, we handle the complexity so your team can focus on the product.
AI that fits
your workflow
We integrate large language models into your existing products and processes — not as a gimmick, but as a force multiplier for your team.
From customer support automation to internal knowledge search, every integration is designed for reliability, cost efficiency, and measurable ROI.
Integration capabilities
RAG pipelines
Retrieval-augmented generation grounded in your knowledge base. Vector search, embedding pipelines, and source attribution for accurate, verifiable answers.
Function calling
LLMs that invoke APIs, query databases, and trigger workflows based on user intent. We build the tooling layer that makes this safe and reliable at scale.
Fine-tuning & evaluation
Domain-specific fine-tuning with automated evaluation pipelines. Every prompt and model change is tested against accuracy, latency, and cost benchmarks.
Integration
results
Our LLM integrations ship faster and cost less than building in-house. Production-grade reliability with sub-200ms latency at scale.
Every deployment is monitored for accuracy, cost, and performance so you can demonstrate ROI from day one.
Tier-1 deflection at 38%
A RAG agent grounded in the help center and ticket history. Answers cite their sources, escalate to humans on low confidence, and learn from every resolved chat.
Internal search that finally works
Embeddings across Notion, Drive, and Slack with permission-aware retrieval. New hires onboard in days because answers come back in seconds, not days.
Function-calling that ships
The model triggers real APIs — refunds, lookups, account changes — behind a guardrail layer with eval suites and human-in-the-loop on anything reversible.
Routing that pays the bill
A small model handles the easy 80%, a frontier model picks up the rest. Token spend dropped 47% without any drop in measured response quality.
From architecture
to production
A structured path from model selection to live deployment. We handle the complexity of prompt engineering, RAG pipelines, and production hardening.
Every step includes evaluation benchmarks so you know exactly how the system performs before it reaches your users.
Pick a single use case
We define one workflow worth automating, write the success metric, and agree on the budget for tokens, latency, and acceptable error rate before any code lands.
Prompts, retrieval, and tools
Prompt templates in version control, retrieval tuned against a labeled set, and a tool layer with strict schemas. Every change is gated by an evaluation harness.
Offline and online evals
Golden datasets, LLM-as-judge for nuanced cases, and dashboards that watch live traffic. We catch regressions before users notice they slipped.
Ship behind a kill switch
Canary rollout to 5% of traffic, automated rollback on guardrail breach, and a runbook your on-call can actually follow at 2 AM with no context.
Transparent pricing
Fixed-price projects for well-defined scopes, time-and-materials for ongoing work. No hidden fees, no surprise invoices — every engagement starts with a clear scope and budget.
Whether you need an MVP from 2 weeks or a full product team for a year — we scale our engagement to match your stage, budget, and ambition.
Scoped to you
LLM integration into your product
Prompt engineering · API layer · Cost optimization · Guardrails
Enterprise
everything in one package — from research to go-to-market
Research · Design · Engineering · Growth
