Frontier research,
shipped as product.
We train our own specialized industry models and the agentic systems that run on them. Our research moves straight from the lab into production — no middlemen, no generalist chat wrappers.
Introducing Nico 2.5 — a frontier model, built for revenue.
A 120B-parameter mixture-of-experts post-trained on real outbound, real replies, real deals — paired with live retrieval over our knowledge graph at inference. A specialist that beats generalist chat models on every revenue task that matters.
Read paperIntroducing Nico 2.5
Macrodeep's first frontier model, purpose-built for go-to-market. Architecture, training stages, retrieval-native inference.
Training data at scale
How we sourced, deduplicated, and quality-scored the corpora behind Nico 2.5 — and why the next frontier is data, not compute.
Teaching models to reason
Multi-stage post-training against verifiable outcomes. Why preference optimization against real revenue signals beats generic RLHF.
Synthetic training data
When supervised data runs out, we generate it. Sandboxed agentic trajectories at a scale no public crawl can match.
Agentic code generation
Beyond autocomplete: what it takes for an agent to plan, edit, test, and ship inside a real repository end-to-end.
Multi-agent coordination
A fleet with one brain — how twenty specialized agents share typed memory and produce coherent behavior across months of deal history.
Domain memory
What belongs in the context window versus what belongs in a retrievable graph. The architectural question behind every long-running agent.
Human in the loop, by design
Trust-gated execution. Why the best autonomous systems ship with explicit approval surfaces, audit trails, and reversible writes.
The iPhone moment for sales
Wiring twenty agents, deep research, multi-channel outreach, and healing goals into one continuously-learning autonomous system.
Cross-channel intelligence
Signal aggregation across email, LinkedIn, web, and funding. Why a multi-channel multiplier is the right prior for B2B intent.
From reactive to proactive
The shift from agents that respond to prompts to agents that watch, decide, and act on their own triggers.
Work with us.
We're hiring researchers and engineers pushing the frontier of autonomous revenue AI. Read what we publish, then come build what we don't.