Update Regarding Q3 $SERV Distribution:
The snapshot window will take place from July 1st to August 15th. Claims for all users will be available starting August 22nd (12:00 PM) UTC.
The Drop-in
Swap one line.
Keep your agents.
OpenAI- and Anthropic-SDK compatible
2-minute integration
No vendor lock-in
OpenAI SDK
Anthropic SDK
import OpenAI from “openai”;
const client = new OpenAI({
baseURL: “https://inference-api.openserv.ai/v1”,
apiKey: process.env.SERV_API_KEY,
});
const completion = await client.chat.completions.create({
model: “gpt-5.4-mini”,
messages: [
{ role: “system”, content: “You are a concise assistant.” },
{ role: “user”, content: “What is a CPU register?” },
],
});
console.log(completion.choices[0].message.content);
OpenAI
Production signals
from SERV Reasoning users.
Lower cost, fewer failed calls, and more reliable execution across real agent workloads.
107x
107x
performance-per-dollar
Independent benchmark. With SERV, small models outperform frontier.
ThoughtProof PLV · May 2026 · ThoughtProof
0
0
failed calls
Private-beta production workload with no failed calls recorded.
1 month private beta · 100K+ requests · Neol
90%
90%
cost reduction
Lower inference cost while preserving agent output quality.
Agentic OS for food industry · GastroSight
The Agent Trust stack.
Built for enterprise.
Auditing and privacy surface agents need to enter regulated workflows.
V 2.1
TEE + E2EE
Trusted-execution-environment private inference for regulated data.
NEXT
V 2.4
Graph Sharding (Audit)
Every reasoning step traceable. Audit-grade decision trails.
NEXT
-
SOC 2
Type I targeted Q3 2026 · Type II Q1 2027.
IN PROGRESS
-
Data residency
EU, UAE, and on-premise options for enterprise contracts.
AVAILABLE
Why this exists
LLMS fail in production.
Raw intelligence is not enough.
Agents are costly
One request becomes a chain of calls.
Agents split work into steps, retries, tool calls, and validations. Without boundaries, latency and cost compound fast.
Agents are not reliable
Prompts do not enforce behavior.
Enterprise workflows need structured outputs, failure handling, and repeatable reasoning paths instead of best-effort prose.
Agents are a black box.
You can't trace agent's decisions.
Regulated teams need traceability, private inference options, and clear execution logs before agents can touch real operations.