Internal knowledge assistant
Answers questions from your docs, wikis, Slack and tickets, with citations, so people stop hunting for information.
We measureTime to answer, adoption, answer accuracy
AI agents that do real work, safely
We design, build and run AI agents, copilots and knowledge systems that plug into the tools your team already uses. Every agent ships with evaluation, guardrails and cost monitoring, because a demo is easy and production is not.
What we build
- 01Custom AI agents and multi-agent workflows
- 02RAG over docs, tickets, CRM and databases
- 03Customer-facing copilots and chat interfaces
- 04Voice agents for support and scheduling
- 05LLM integrations: OpenAI, Claude, Gemini, open-source models
- 06Evaluation suites, guardrails and human review steps
(01)Where it pays off
Where agents pay off first
These are the workflows where we usually start. Each one has a clear metric, so you can tell quickly whether it is working.
Support automation
Resolves routine tickets, drafts replies for agents and routes complex cases to the right person with full context.
We measureDeflection rate, first-response time, CSAT
Sales and lead qualification
Researches inbound leads, scores fit, books meetings and drafts personalized follow-ups for your reps to approve.
We measureQualified leads per rep, response time
Document processing
Extracts, classifies and checks contracts, invoices and forms, flagging exceptions for human review.
We measureProcessing time per document, error rate
(02)Process
How the engagement runs.
- 01
Discovery and scoping
We map the workflow, collect real examples and agree on the metric the agent has to move.
- 02
Architecture and model choice
We choose models, retrieval and tool design based on accuracy, latency, cost and data privacy needs.
- 03
Build with an evaluation set
We build against a test set of real cases from day one, so every change is measured, not guessed.
- 04
Guardrails and review
Input and output checks, permissions, audit logs and human approval for high-stakes actions.
- 05
Launch and monitor
Production rollout with tracing, cost dashboards and a feedback loop that keeps accuracy improving.
(03)Models and tools we work with
- OpenAI/
- Claude/
- Gemini/
- Llama/
- LangChain/
- LlamaIndex/
- CrewAI/
- Pinecone/
- Weaviate/
- pgvector/
- Ollama/
- LangSmith
We're pragmatic about tools. If your team already runs something that works, we build on it.
(04)FAQ
How long does it take to build an AI agent?
A focused, single-purpose agent usually reaches production in two to three weeks. Multi-agent systems with several integrations typically take six to eight weeks from discovery to launch.
What does it cost to run an agent?
Running costs are mostly model API usage and vector database hosting, and depend heavily on volume. We estimate them during discovery, set budget alerts and optimize prompts, caching and model choice to keep them predictable.
Can the agent connect to our existing tools?
Yes. We regularly integrate with Slack, Microsoft Teams, Salesforce, HubSpot, Zendesk, Notion, Confluence, Google Workspace, internal databases and custom APIs.
How do you make sure the answers are accurate?
We ground answers in your data with retrieval and citations, test every change against an evaluation set of real cases, and add human review for decisions that carry risk.
Is our data safe?
We can run on enterprise model endpoints that do not train on your data, keep data in your own cloud account, apply role-based access to retrieved content and log every agent action for audit.
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