Every request rebuilds context from scratch, fires tool calls the task doesn't need, defaults to the most expensive model, and leaves no record of why. The models aren't the problem. The plumbing around them is.
~95% of enterprise GenAI pilots show no measurable return — MIT NANDA, 2025
THE PROBLEM
The agent is starved, not flooded — the knowledge it needs sits behind permissions the model never sees.
Every team rebuilds the same context. Nothing persists across them.
No audit of what entered the prompt, which model answered, or what it cost.
Nothing can leave the building — and the platform assumes everything will.
THE LAYER
SILICON & COMPUTE — CLOUD AND ON-PREM ACCELERATORS
Inside the x25 layer
The agent keeps its tools. The model stays the customer's choice. The data never leaves.
WHAT IT DOES
Reaches the knowledge the agent can't, ranks it, caches it, and persists it across teams so the second request is cheaper than the first.
Pre-processes the prompt and cuts the tool schemas the task doesn't need, so fewer calls reach your MCP servers and fewer tokens reach the model.
Sends each task to the best-fit model on quality, cost and latency, across providers. Neutral: we never route toward our own models, because we don't have any.
Every decision logged with model, cost, latency, quality score and rationale in a tamper-evident record. Permissions enforced before hydration.
Same answer. Fewer tool calls. Fewer tokens. A record of why.
INTEGRATION
Every mode runs in the customer's own environment.
On-prem or private cloud. Their collector, their vector store, their weights. Nothing about the architecture assumes data leaves.
› classify 4,000 support tickets
routed → small open-weight · quality 0.91 · 280ms · $0.0003 per task
› summarize Q3 trial safety data
routed → compact frontier · quality 0.88 · 412ms · 92% below default
› draft regulatory submission language
routed → flagship frontier · quality 0.97 · 1.2s · escalated for quality
This is Narrow, Route and Prove in production. Assemble ran upstream, before the first token.
MEASURED
38%
FEWER TOOL CALLS
PER TASK
42ms
DECISION OVERHEAD
PER REQUEST
0.84
MEAN QUALITY SCORE
ACROSS PRODUCTION TRAFFIC
MEASURED ON PILOT WORKLOADS AT ANSWER PARITY — REDUCTION ONLY COUNTS IF QUALITY HOLDS.
Winner, MIT CSAIL Agentic AI Hackathon.
THE LOOP
OBSERVE
Real production traces, tool calls, tokens, latency
ASSEMBLE
Context built and narrowed per task
MEASURE
Answer parity — reduction only counts if quality holds
LEARN
Per-tenant rankers and classifiers, trained on the customer's own telemetry
OWN
Distilled open-weight models the customer keeps
The artifacts are per-tenant and never leave. Nobody can cold-start what took a year of your traffic to learn.
TRAINED ONLY ON OPERATIONAL TELEMETRY AND OPEN-WEIGHT GENERATIONS
WHO IT'S FOR
The pain lands fastest with technically mature platform teams — the ones with real traffic, real bills, and a regulator or board that will eventually ask why.
You own the gateway, the vector store and the bill. x25 gives you caching, cross-team context, routing and an audit trail in three lines of code, in your environment.
When someone asks "why did the AI decide that," the record already exists and the data never leaves.
Your agent keeps its own tools. x25 sees the schemas and results; it never dispatches. Fewer calls reach your MCP servers, at answer parity.
PILOTS LIVE ACROSS REGULATED ENTERPRISES — EDUCATION, TELECOM, AVIATION, SUPPLY CHAIN, RESEARCH.