I
Domain parity or better
The specialist matches or exceeds frontier models on scenarios designed for your vertical.
Specialisation Control Plane · v0.9Trusted specialist agents
Trusted specialist agents
for the workflows
that decide your business.
Agentsia is the specialisation control plane for enterprise model fleets. Purpose-trained specialist SLMs that match or exceed frontier AI on your narrow commercial workflows, at lower latency and a fraction of the inference cost. Trained and governed in your environment. Served on the inference substrate you choose.
194
Eval scenarios
58
Safety nets
10×vs frontier
Inference savings
< 200ms p95
Latency budget
Position
The moat lives above the substrate, not on it.
Groq, Cerebras, Fireworks, Together — they optimise the execution of a model. Agentsia decides which specialist should exist, how to evaluate it rigorously, when to promote it, and how a fleet of specialists compounds into a durable moat.
Infrastructure improvements at the substrate benefit us without commoditising us.
L3
Application
Your product · workflow automation · embedded intelligence
L2
Agentsia
Specialist creation · evaluation · promotion · rollback · fleet routing · lineage
L1
Substrate
Inference runtimes · training compute · hardware · serving infrastructure
Fig. 01 · Specialisation control plane
The Loop
A closed eval–train cycle that runs without supervision.
You set the target composite. You review novel failure modes. Modelsmith handles everything else — classification, data generation, adapter training, rollback on regression, and scenario proposal.
01
Evaluate
Run governed scenarios. Composite across core, robustness, micro-benchmarks.
02
Diagnose
Classify failures. Flag never-pass (SFT), flip-flop (held-out), always-pass (excluded).
03
Train
Generate augmented training from failures. SFT warm-up. GRPO from reward.
04
Re-score
Score new adapter. Regression >10% triggers automatic rollback.
05
Propose
Persistent patterns generate new eval scenarios. Staged for your review.
Five Compounding Advantages
Concrete proof, not vague promise.
Generate a synthetic eval suite from publicly available domain knowledge. Run it against the leading frontier models and an Agentsia specialist. The delta is measurable along five axes that compound.
II
Fraction of the cost
Serving a small specialist on the right substrate costs far less than routing through frontier APIs.
III
Lower latency
Specialist SLMs hit sub-second budgets on your chosen cloud or on-prem inference vendor.
IV
Data stays yours
Training and evaluation run in your controlled environment. Even air-gapped. Even under residency rules.
V
Less architectural rope
Stable domain knowledge is trained into weights, not shoehorned through retrieval pipelines at every query.
Config-driven onboarding
A new specialist is a single JSON diff.
No shell script to copy. No compose file to hand-edit. Every script, compose file, and iterate loop reads from the model profile and generates the appropriate behaviour at runtime.
Agents can onboard a new model by writing JSON, not by copy-pasting shell.
config/clusters.jsonjson
{
"qwen3_32b": {
"hf_id": "Qwen/Qwen3-32B-AWQ",
"architecture": "dense",
"quantization": {
"format": "awq",
"kv_cache_dtype": "turboquant35"
},
"training": {
"method": "grpo",
"sft_warmup": true,
"lora_targets": ["q_proj", "k_proj", "v_proj", "o_proj"],
"max_completion_length": 1024
},
"clusters": ["exchange", "gaming", "campaign", "trust"],
"promotion_gates": {
"target_composite": 98,
"max_regression_pct": 10
}
}
}Read
Nothing here is proprietary reading.
We prefer to compete on substance. The moat lives in the operating system, not in the prose.
015 min
Thesis
Five claims on why specialists beat prompt-wrappers around frontier models.
Read→
0218 min
Vision
The full argument. Market position, moat, and operating model.
Read→
039 min
Modelsmith
The product. Evaluation-first specialisation engine.
Read→
0412 min
Approach
Seven pillars, in compounding order. Honest status.
Read→
058 min
Roadmap
Five phases from wedge to fleet.
Read→
066 essays
Library
Field notes on eval design, promotion discipline, and the fork workflow.
Read→
You hold the proprietary data.
We build the operating model.
Engagements begin with a fork of the Modelsmith repository, running inside your approved environment from day one, with platform improvements flowing back upstream.