• 10 min read
Stop Approvals Getting Lost in Email with Microsoft Teams
Email approvals get buried, missed, and never close the loop. Jinba routes structured Adaptive Cards to Teams, captures approve/reject in-channel, and logs every decision.
Recent Posts
• 19 min read
4 Best AI Agent Platforms for Banks (2026)
Banks running pilots across multiple agent frameworks with no shared audit trail need fleet governance, not another chatbot builder. This list covers the 4 best AI agent platforms for banks in 2026 — Jinba, Kore.ai, Fiserv agentOS, and Rasa — ranked against governance and audit control, deployment and data residency, core-system connector reach, and pricing transparency.
• 19 min read
Jinba vs Claude Cowork: A Workflow Platform vs a Personal AI Agent
Claude Cowork has no audit logs, no RBAC, and no SSO — and Anthropic explicitly says not to use it for regulated workloads. Jinba is SOC 2 Type II, HIPAA, and GDPR-compliant, with live audit logging, role-based permissions, SSO enforcement, and on-premise deployment. This comparison breaks down exactly where each product belongs in a regulated enterprise's stack.
• 13 min read
5 Shared AI Memory Tools for Enterprise Teams, Ranked by Compliance
Shared AI memory tools for enterprise teams cannot be chosen on architecture alone. Once memory holds customer, patient, or financial data, certification, deployment model, and access control become the gating decision. This ranking evaluates Zep Cloud, Mem0, Cognee, Letta, and Graphiti on the four criteria that dominate a security review: certification standing, data residency, access control and audit trail, and architecture maturity.
• 18 min read
4 Best AI Agent Governance Platforms for Banking Compliance in 2026
The April 2026 OCC/Fed/FDIC rewrite of SR 11-7 carved agentic AI out of the "model" definition — while DORA treats every AI agent as an ICT system. Banks are now assembling a governance stack rather than buying one off the shelf. This guide ranks 4 agent governance approaches — Jinba, Fiddler AI, AI GRC platforms, and data-governance tools — against the criteria that matter: regulatory mapping to named regimes (SR 11-7, DORA, GLBA, fair-lending), deployment fit (on-premise, air-gapped, or cloud), stack coverage, and vendor maturity.
• 17 min read
Shadow AI in Banking: How to Detect, Govern, and Replace It
Shadow AI in banking is already in production — inside loan ops, compliance, and relationship management — and FINRA's 2025 guidance puts all of it inside the scope of supervision. This framework covers the three-step sequence: detect every unsanctioned AI use touching bank data, govern it with a tiered policy that survives real workflows, and replace ungoverned tools with sanctioned alternatives before a gap becomes an examination finding. Includes a risk-tier table and a reusable compliance template.
• 11 min read
Best Dust AI Alternatives for Regulated Enterprises (Compliance & On-Premise)
Dust's compliance envelope stops at SOC 2, GDPR, and HIPAA-enabling status — no ISO 27001, no FedRAMP, no self-hosted or air-gapped deployment. This guide evaluates the Dust AI alternatives that actually clear enterprise security reviews: Jinba for on-premise deterministic workflows in banks and health systems, Onyx for per-user permission sync across SharePoint and Google Drive, and Dify for engineering teams building custom AI apps with full control over their own compliance attestation.
• 18 min read
How to Stop Shadow AI at Your Bank
Shadow AI in banking is not a policy problem — it is a replacement problem. Employees paste customer names, SSNs, and credit files into free ChatGPT and Gemini accounts because no approved alternative is faster than the workaround. This guide covers what shadow AI is, why banking faces sharper regulatory exposure than any other industry, which GLBA Safeguards Rule obligations it triggers, how detection tools locate existing exposure, and — the step most guidance skips — how to deploy a governed AI platform that employees will actually use instead of the banned tools.
• 17 min read
Model Routing vs. Deterministic Workflows: Which Actually Cuts Enterprise AI Costs
Model routing cuts per-call inference costs — RouteLLM benchmarks show 85% savings on MT Bench at 95% GPT-4 quality — but production teams still pay $1,000–5,000 a month in LLM API fees. Deterministic workflows run the same workload for $50–200 a month by eliminating inference on paths that never needed a model. This piece works through the cost mechanics, the benchmark caveats, and the decision rule for choosing between them.