Governed. Repeatable. Production by Default.

Why Enterprises Stall After the First AI Win

Most organizations can build a “hero agent.” Few can build a program.
They succeed once, then stall. Why?

• No Factory: bespoke scaffolding, no reuse → every new agent starts from scratch.
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• Governance Gaps: fragmented teams → inconsistent policies, unclear lineage, audit pain.
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• No Cost Visibility: latency and model choices unmeasured → unpredictable bills.
  
  

By 2025, trust and governance remain the top blockers to GenAI scale. Every delayed month is lost competitive advantage—and rising risk.
AscendAI fixes this with a repeatable factory model that operationalizes governance, automation, and FinOps across the agent lifecycle.
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What Is AscendAI: Agent Factory?
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A Databricks-native accelerator that industrializes AI agent delivery.
It unifies Databricks’ Data Intelligence Platform, Lakehouse, Unity Catalog, Vector Search, and Mosaic AI with Koantek’s proven IP: automation templates, policy packs, and DevOps blueprints.

You Get

• <3 Weeks to Value: Prebuilt templates + automated code generation = first agent live in weeks, not quarters.
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• Governance by Design: Unity Catalog + Mosaic AI Gateway enforce lineage, masking, and audit from day one.
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• Predictable Costs: FinOps metrics like $/1K tokens and p50 latency make optimization continuous.
  Up to 60% Lower TCO: Through 80%+ automation, model right-sizing, and unified monitoring.
  
  

How It Works: The Five-Stage Factory

1. Delta → Vector Retrieval Fabric

Ingest enterprise data into Delta tables, index with Vector Search, and govern via Unity Catalog lineage and tags.
Every embedding and RAG query is auditable by design.

2. Declarative Pipeline Orchestration

Databricks Lakeflow + Databricks Asset Bundles (DABs) orchestrate ingest, fine-tuning, evaluation, and deployment.
All infrastructure and workflow definitions are as code (Terraform + YAML) for CI/CD consistency across dev → test → prod.

3. Automated Agent Scaffolding

Start from proven agent templates—Q&A, Data Extractor, SQL Generator, Decision Agent, and more.
Koantek’s coding agents generate 70%+ of the boilerplate (project setup, logging, error handling). Engineers focus on innovation, not plumbing.

4. Compliance Template Library

Apply pre-built policy blueprints per agent: PII/PHI masking, usage quotas, audit logging, and row/column-level security.
Mosaic AI Gateway enforces runtime safety—blocking unsafe content and logging every event.
Out-of-box coverage: 80–90% of required controls.

5. Code Generation & Deployment Factory

One push builds CI/CD, Terraform infra, MLflow tracking, model serving, and optional Streamlit or DBSQL UI.
Promotion gates verify UC permissions, feature registration, and audit readiness before production.

Engineer-Credible AI Under the Hood

• Multi-Agent Orchestration: specialized agents coordinate via shared memory for retrieval, summarization, validation, and reasoning.
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• Two Build Paths, One Backend:
  
  
  • Agent Bricks (UI): low-code assembly for quick wins.
  • DSPy (Code-First): fine-grained control for complex orchestration.
    Both feed into the same logging, monitoring, and governance stack.
    
    
• Continuous Evaluation: MLflow logs metrics and AI judges score responses on quality, cost, and latency. Teams commonly see +26-point accuracy gains after iterative loops.
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• Mosaic AI Gateway: enforces RBAC, rate limits, PII scrubbing, and unsafe content filters at runtime.
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• FinOps by Default: instrumentation exposes $/token, latency percentiles, and model utilization—surfacing optimization opportunities automatically.
  
  

Security, Governance & Compliance: Built-In, Not Bolted-On

• Unity Catalog Everywhere: unified lineage and policy propagation from Bronze → Silver → Gold datasets, features, and embeddings.
• Regulatory Policy Packs: HIPAA, ISO 27001, Basel III templates as code ensure repeatable, testable controls.
• Hardened DevOps: workspace isolation, secrets management, promotion gates—all ISO/IEC 27001 aligned.
• Threat-Modeled QA: STRIDE-aligned checks, prompt-injection simulations, and optional human-in-the-loop reviews for sensitive tasks.
  
  

Outcomes you can take to the board

• ~90%+ governance coverage across agent activity → fewer audit gaps; clear approvals and lineage for every answer.
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• Up to 60% lower TCO via automation, right-sizing models/infra, and consolidated tooling.
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• Material performance lifts: e.g., 59% → 85% answer accuracy after fine-tuning and iterative eval; 2.5× latency improvements with optimized serving.
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• Speed as a strategy: <3–4 weeks to MVP, ~6 weeks to scale-out hardening and 2–3 agents, ~12 weeks to a governed fleet and ROI/NPV proof.

“We went from talking about AI to having it in production in under a month governed, measured, and cheaper than our previous approach.”

Delivery model & timeline

• Phase 1 – MVP (≤ 3–4 weeks): First agent live in prod, factory configured in your Databricks workspace, baseline SLOs/FinOps metrics, bundle + runbook delivered.
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• Phase 2 – Scale-Out (~6 weeks): Multi-env CI/CD, monitoring dashboards, policy packs tailored, +1–2 additional agents, enablement for internal teams.
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• Phase 3 – Fleet & Optimize (~12 weeks): Dozens of agents, ~90% governance coverage, advanced autoscaling, bespoke eval metrics, and formal ROI/NPV pack.

Interoperability Without Lock-In

• Data: Delta tables, Vector Search, federated access (SharePoint, Confluence, etc.).
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• Models: Bring your own—Databricks-hosted, open source, or licensed APIs via Gateway.
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• Tooling: Works with GitHub, Azure DevOps, Jenkins, Terraform, Splunk, and enterprise SSO.
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• Apps & BI: Expose agents as APIs or apps; connect to Slack, ServiceNow, Salesforce, Power BI, Tableau—no black boxes.
  
  

Where It’s Working

• Telecom | Customer Support Virtual Agent: 40% faster resolutions; full audit trail via UC.
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• Financial Services | Research Q&A: accuracy up from 59% → 85%, latency cut 2.5×.
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• Manufacturing | Forecasting Assistant: planners save days per week; all outputs logged for explainability.
  
  

What’s at Stake and How to Start

Without a factory model, AI teams burn time on plumbing and invite compliance risk.
With AscendAI Agent Factory, leaders in Data & AI deliver governed speed their boards can trust.

Next Steps

1. Explore a Use Case: Join our free AI Opportunity Workshop to assess impact and feasibility.
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2. See It Live: Book a demo or hands-on PoC in your Databricks workspace.
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3. Align Scope: 1–2 hr architecture session → roadmap, milestones, and effort.
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4. Secure Buy-In: Use our Executive Launch Kit (slides + one-pager) to brief stakeholders—we’ll join to validate ROI, security, and integration.
   
   

Contact: sales@koantek.com