Ship AI Agents With Governed Actions & Evidence.
Schema-validated actions, approval gates for high-risk steps, and an exportable evidence trail for every run. Keep
Deployment options: Cloud SaaS · Private VPC · On-Premise
Bring your own model
Keep your provider. DhakaBrain governs execution and captures exportable evidence.
Trigger any Workflow
Why AI projects die in the lab
You cannot audit a probability.
So we audit decisions, actions, and approvals — with exportable evidence.
Regulated teams can’t scale LLMs on “likely correct.” They need action governance (schemas + approvals), traceability (evidence), and policy-scoped access control.
Standard "Black Box" RAG
- Undefined Liability: If the AI lies, you can't explain why. You have no log of what data was retrieved to justify the error to a regulator.
- Dangerous Autonomy: A model can propose side-effects (refunds, transfers, closures) without reliable guardrails or approvals.
- Customer Amnesia: The AI forgets everything when the chat closes. Users are frustrated because they have to repeat their context every time.
DhakaBrain Action Control Plane
- Evidence Trail: Log the exact context snapshot (inputs + retrieved sources) plus the decision path—exportable for audits.
- Governed Actions: Validate action payloads against strict schemas and route critical actions to explicit approval before execution.
- Stateful Orchestration: Multi-turn slot filling and safe retries—without hardcoding brittle agent logic.
Your Path to Production
Control actions. Prove decisions.
Don’t rebuild agent infrastructure. Add an action control plane that makes execution governable in production.
- Policy-Scoped Context (Access Control)
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Ground agents on approved sources only. DhakaBrain scopes retrieval by policy and mirrors your Access Control (RBAC/ABAC) so confidential data never leaks to the wrong user or agent.
- Action Registry + Schema Validation
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Register actions with strict JSON schemas. Every proposed action is validated before it can trigger tools, workflows, or internal APIs.
- Human-in-the-Loop Control
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Choose which actions require human approval (e.g., transfers, refunds, account closure, data exports). Approval-required actions are frozen and routed to an approval queue before execution.
Enforceable Governance
Context Packs you can defend.
From “prompting” to governed execution.
DhakaBrain produces a Context Pack (policy-scoped sources) and an execution plan for actions. Governance is policy-driven (schemas, approvals, logs) while generation stays model-driven. The model’s output is treated as a proposal: validated, optionally approved, and always traceable.
Seamless Integration
The Architecture of Trust
Tip: click any block to see what changes in Before vs With DhakaBrain.
n8n
Zapier
Triggered directly by your backend. Triggered only after DhakaBrain validation.
Inspect What the Agent Saw
The Context Inspector.
Audit retrieval, generate Context Graphs, and verify the evidence behind decisions. Use it to validate policy scope and evidence before production. Manage exactly what the agent can access—and what it must forget.
- Search & Visualize
- Run hybrid searches on your data and generate visual Context Graphs to verify how your AI connects the dots.
- Surgical Data Control
- Need to remove a sensitive document? Modify or Forget specific data items instantly — supports right-to-erasure workflows (e.g., GDPR Article 17).
- Knowledge Insights
- Visualize your data landscape. Track document distribution and ingestion stats to ensure your Knowledge Base covers exactly what it needs to.
A Control Plane, Not a Wrapper
Integrate fast with your existing stack.
DhakaBrain is a specialized middleware. Query our API to get a Context Pack (policy-scoped sources + optional memory) and governed action proposals, then pass it to your existing LLM chain. We don’t replace your model; we make execution governable and auditable.
import os, json
from openai import OpenAI
from dhakabrain import DhakaBrain
dhakabrain = DhakaBrain(api_key=os.environ["DHAKABRAIN_API_KEY"]) # dk_agent_…
llm = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
ctx = dhakabrain.get_context(message=user_input, user_id="cust_88", conversation_id="session_123")
prompt = dhakabrain.build_prompt_for_llm(system_prompt="You are a compliant assistant.", recent_messages=chat_history, context=ctx)
out = llm.chat.completions.create(model="gpt-4o", messages=[{"role": "system", "content": prompt}], response_format={"type": "json_object"} if ctx.action_detected else None)
content = out.choices[0].message.content or ""
if ctx.action_detected:
# action proposal → schema validation → optional approval → execution
p = json.loads(content)
dhakabrain.execute_action(rule_name=p["rule_name"], parameters=p.get("parameters", {}), conversation_id="session_123", user_id="cust_88", run_id=ctx.run_id)
else:
print(content)
dhakabrain.track(run_id=ctx.run_id, response_text=content)Explore the Platform
Whether you're a business leader, a product manager, or a hands-on engineer, we have a path for you.
Explore Features
Get a detailed, step-by-step overview of the core components of the DhakaBrain engine.
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Discover Solutions
See how governed execution is applied to solve real-world problems in your industry.
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For Engineers
Dive into the API. See interactive code examples and learn how to start building.
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Sign up for our generous free tier. Build unlimited agents with 100,000 AI Credits each month, and enjoy free, unlimited use of the Context Inspector for all your testing and analysis. No credit card required.