All models
personaplan slot only
Legal counsel persona
persona-legal-counsel
Cautious, cites statutes, flags jurisdiction; never gives definitive legal advice.
Decision and plan target (no provider attached) PII allowed
- Price per 1M tokens
- free
- $0 per 1k
- Requests · 30d
- 0
- no traffic
- Latency
- 0 ms
- declared
- Success rate
- n/a
- quality prior 0.80
Traffic over 30 days
Requests per day this target answered on this deployment, with the cost line.
No requests yet. Route something from the playground to see usage here.
- Tokens
- 0
- Spend
- $0
- Previous window
- 0 req
Routed for
Domains the signal layer detected on requests that ended here.
No routed requests yet. Declared domains: Legal.
Capabilities
DomainsLegal
ActionsReasoning, Summarize, Qa, Extract
Complexity band0.00 - 1.00
Languagesen
Context windowundeclared
Streamingyes
Policy constraints
Hard stops enforced before scoring.
Data boundaryPublic
Regionsanywhere
PIIallowed
Max input tokensunlimited
Tenantsall
Representative prompts
Examples the similarity strategy matches against.
- Review this NDA clause for liability exposure under GDPR.
- Is this indemnification clause enforceable in California?
Route to it
This persona is not executed by the platform. Ask the router for a decision restricted to kinds=["persona"] and dispatch the winner in your own stack; send feedback afterwards so the learners improve.
curlbash
curl https://api.opensmartroute.ai/api/v1/route \
-H "Authorization: Bearer $OSR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"text": "Review this NDA clause for liability exposure under GDPR.", "kinds": ["persona"], "top_k": 3}'
# Returns the chosen target, confidence, the ranked alternatives and the
# per-strategy trace. Execution of persona targets happens in your stack.httpxpython
import httpx
r = httpx.post(
"https://api.opensmartroute.ai/api/v1/route",
headers={"Authorization": "Bearer osr_live_..."},
json={"text": "Review this NDA clause for liability exposure under GDPR.", "kinds": ["persona"]},
)
d = r.json()
print(d["target"]["id"], d["confidence"])
print([t["id"] for t in d["ranked"]]) # e.g. persona-legal-counsel first when it fits