AI Agents & Automation
We build AI agents and automation that plug into your operations — LLM-powered assistants, workflow automation and retrieval systems using OpenAI, Claude and LangChain, wired to your real data.
What we deliver
Grounded LLM assistants
Chat and task agents on OpenAI or Claude, grounded in your own content and tone through a retrieval layer rather than fine-tuning, so an update is a re-index, not a retraining bill. We keep the model behind an interface so swapping providers is a config change, not a rewrite.
Retrieval (RAG)
Vector search over your docs and data with chunking and metadata filters tuned to your corpus, so answers cite real sources instead of confabulating. We measure retrieval quality (recall@k) separately from generation, because most "the AI is wrong" bugs are actually retrieval bugs.
Workflow automation
Agents that take action — draft the email, update the CRM, book the slot — across your existing tools, with every side-effecting step idempotent and logged. Long-running work runs as durable jobs so a timeout does not double-charge a customer.
Tool & API calling
Function calling wired to your real APIs with strict typed schemas and validation on the arguments the model produces, because an LLM will happily invent a parameter. Destructive tools sit behind confirmation and permission checks, not blind trust.
Guardrails & evals
Input/output moderation, prompt-injection defenses and a graded eval suite that runs on every prompt change, so "we improved the prompt" is a number, not a hunch. Regressions are caught in CI against real queries before users see them.
Cost & observability
Per-request token accounting, model routing (cheap model for easy turns, frontier for hard ones) and caching to keep spend predictable. Full traces of prompts, tool calls and latencies mean you can debug a bad answer instead of guessing.
How we work
- 01
Discovery
We pick the highest-value use case and define what "good" looks like with real examples and failure cases up front. A vague "add AI" turns into a measurable task with an eval target before any code.
- 02
Data & prompt design
We prepare and chunk your knowledge base, design the retrieval strategy and write prompts with explicit refusal and escalation rules. Sensitive-data handling and PII redaction are decided here, not patched later.
- 03
Build & evaluate
We build the agent and grade it against a held-out set of real queries, tuning retrieval and prompts until accuracy and cost hit target. Tool calls run against sandboxed integrations before they touch production data.
- 04
Deploy & monitor
We ship behind tracing, cost alerts and a human-handoff path for low-confidence turns. Live traffic feeds new eval cases, so the agent gets measurably better instead of silently drifting.
Tech we use
- Models
- OpenAIClaudeGeminiLlamaMistralDeepSeek
- Orchestration
- LangChainLangGraphLlamaIndexMCPFunction callingMulti-agent
- Retrieval (RAG)
- pgvectorPineconeWeaviateQdrantEmbeddingsRerankersHybrid searchChunking
- Patterns & prompting
- ReActTool-useStructured outputFew-shotChain-of-thoughtHuman-in-the-loop
- Runtime & serving
- PythonFastAPINode.jsvLLMDurable jobsQueuesStreaming (SSE)
- Data & tuning
- Fine-tuningLoRADistillationSynthetic dataVector ETL
- Evals & safety
- Evals en CILLM-as-judgeRed-teamingGuardrailsPII redactionPrompt-injection defense
- Ops & cost
- LangSmithTracingToken accountingModel routingSemantic cachingRate limiting
What you get
- Agent service with your prompts, tools and retrieval index in your Git org
- Knowledge-base ingestion and re-index pipeline
- Versioned eval suite with an accuracy/cost report per prompt change
- API endpoint or embeddable widget for your site or product
- Token-cost and quality dashboard with per-request traces
- Prompt, tool and configuration docs you can maintain in-house
Related work
Frequently asked
Which model do you use — and are we locked in?
We pick per use case across OpenAI and Claude, and keep the model behind an interface so switching providers is a config change, not a rewrite. As pricing and capability shift, you can route to whatever wins without touching product code.
How do you stop it from making things up?
Retrieval grounds answers in your real data with citations, and we measure retrieval quality separately so we can tell a wrong lookup from a wrong answer. Guardrails, refusal rules and an eval suite in CI keep hallucinations and off-topic replies from reaching users.
Who pays for model usage, and how do you control cost?
Token costs run on your own provider account, fully transparent. We keep them predictable with model routing, caching and per-request accounting, and set cost alerts so a runaway prompt is caught in minutes, not on the invoice.
Can it actually do things, or just chat?
It acts. Function calling wires the agent to your APIs to send email, update records or book time, with every side-effecting call typed, validated, idempotent and logged — and destructive actions gated behind confirmation.
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