Cheaper and faster agents, with accuracy held to a baseline
Your agent reaches its cost and latency target without losing accuracy. One change at a time, each rerun against a baseline built from your own cases.
Three-agent product advisor guiding customers through print-order configuration.
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HIPAA-compliant guideline-executing triage — 44% to 98% on the open 50-scenario benchmark.
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Text-to-workflow agents building validated node graphs inside the platform.
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Bilingual English/Arabic agent inside ARIJ's Moodle environment, answering only from ARIJ's own knowledge base.
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Supply chain intelligence agents reducing manual coordination overhead.
AI agent implementation for a global automotive enterprise.
Trimmed prompts can lose accuracy, bigger models slow runs, and only a rerun shows it.
Where it stallsA bigger model for every step.
What we do Each step gets the smallest model that passes.
Where it stallsFive changes shipped in one go.
What we do One change at a time, each one retested.
Where it stallsGuessing where time and money go.
What we do Every step priced from traces first.
Every before-and-after is measured on the same set of your cases.
We trace real or recorded runs and price each step: model, tokens, tool calls, waits.
Cheapest changes go first; any change that lowers the baseline score is rolled back.
On the open 50-scenario benchmark, guideline-executing triage scored 98%, against 44% for a raw LLM. Everything lives in your repo with a runbook, so your team tests the next cheaper model.
Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.
Shaping agentic AI
The Linux Foundation formed it around Anthropic's MCP, OpenAI's AGENTS.md and Block's goose. AWS, Google and Microsoft are platinum members. In its own words: “Together, we're shaping the future of agentic AI.”
Open source
Framework contributors
We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.
Research
Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.
Track record
Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.
The agent drafts each workflow and checks it in several steps before it runs, so engineers skip hours of setup.
2 hrsto prepare a workflow by hand
3 minto generate one with the agent
Three agents and 15 tools walk customers through print-order configuration, across more than a billion product combinations.
~20% → ~40%conversion to a final sale
96%+web-to-print orders handled end to end
The model answers the assessment questions. The guideline decides the disposition, the same way every time.
44% → 98%accuracy, raw LLM vs guideline-executing
94%weighted F1 across 591 scenarios
An English and Arabic agent inside ARIJ's Moodle that answers only from ARIJ's own knowledge base.
1% → 100%of inquiries answered, desk vs agent
22countries the network covers
Each path starts with a free call with a founder or senior AI engineer. Changes ship only once they meet the agreed target.
We measure your agent on your own cases and agree the target before changing it.
Best whenYour agent runs, but accuracy, cost or speed falls short.
The model is chosen per step and stays replaceable.
Book a call, and we go through a sample of your runs together.
Retrieval-Augmented Generation (RAG) and context windows enhance AI accuracy—discover how to optimize your AI workflows effectively
Why bother with small models when capable LLMs are available? Because businesses may face stricter requirements than inidividual users
51 tools cost 18,492 tokens per query. Eight discovery strategies — regex, BM25, RAG and more — evaluated on accuracy, latency and token cost.