We build tool-using agents and multi-agent workflows on Claude, GPT and open models. Connected to your systems, grounded in your data, and governed by guardrails, evals and humans in the loop.
Agents are easy to prototype and hard to trust. We engineer for the second part: scoped tools, reliable retrieval, measurable quality and full visibility into every run.
Agents that plan, call tools and complete multi-step tasks - not just chat about them.
Integrated with your APIs, databases, search and SaaS tools through MCP and scoped permissions.
Parameterized tools, validated outputs and human approval for anything consequential.
Every run traced, every change evaluated - so you know when an agent gets better or worse.
Most agent failures are retrieval failures. Our background in Elasticsearch and OpenSearch means we design the search layer your agents depend on - whatever the rest of your stack looks like.
Explore AI SearchKeyword precision plus semantic recall, so agents find the right record, not a near-miss.
Safe, templated queries instead of free-form access to your data.
Agents that join signals across indices, logs and business systems.
Well-described, reusable tools that let agents pick the right capability.
Triage tickets, pull account context and draft or execute resolutions with approval.
Assemble account briefs from CRM, product usage and public sources before every call.
Answer business questions in natural language by querying your systems and explaining the result.
Correlate events across logs, hunt for threats and summarize findings for analysts.
Investigate incidents across telemetry and runbooks and propose next steps.
Extract, validate and route information from contracts, forms and reports.
Choose the workflow, map the tools and data it needs, and define the eval set and guardrails.
A working agent on your real systems, measured against the eval set - evidence, not a demo.
Permissions, human-in-the-loop, tracing, cost tuning, security review and CI-driven evals.
Monitoring, regression evals and new tools as needs grow - or a clean handover to your team.
A chatbot answers questions. An agent works toward a goal: it plans, calls tools and APIs, retrieves information, checks results and takes multi-step actions. Agents combine an LLM with retrieval, function calling and orchestration, within guardrails you define.
We are model-agnostic and work with Claude, GPT, Gemini, Llama and Mistral. We choose based on task quality, latency, cost and privacy requirements, and often mix models within one system. We use MCP to expose your tools and data to agents in a standard way.
Yes. Agents are only useful when they can act. We connect them to your CRMs, databases, search clusters, internal APIs and SaaS tools - with scoped permissions so an agent can only read and do what it is explicitly allowed to.
No. We build agents on whatever stack you run. Search is a strength of ours, so when an agent needs to find information across large or messy data, we can design the retrieval layer - on Elasticsearch, OpenSearch or a vector database - to make it reliable.
Grounding in retrieved facts, structured and validated outputs, scoped and parameterized tools, confidence thresholds, human-in-the-loop approval for consequential actions, and evaluation suites that run on every change. In production, every run is traced so failures can be diagnosed.
Support triage and resolution, sales and account research, data analysis over business systems, document processing, security and operations investigation, report generation and internal workflow automation.
Tell us what it is. In a free 30-minute call we will tell you honestly whether an agent is the right tool, what it would take, and how we would measure success.
