Enterprise-Scale LLM Deployment with Self-Evolving Models and Graph-Based RAG

writer · Finance · 2024 · source Agentic: yes · promotional bias: high · summary-only (no fetched source) · extracted with prompt 0.5.6 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Enterprise generative AI and agent platforms

Writer needed to help enterprises move from chatbots and experiments into production-grade action AI that can automate complex workflows while meeting enterprise demands for ownership, transparency, reliability, secure deployment, and measurable business value.

Constraints:

The source video had no captions, so this extraction relies on the ZenML summary.
Enterprise buyers want control and ownership rather than opaque rented APIs.
Production workflows require audit logs, visibility into inputs and outputs, and monitoring across no-code and code surfaces.
Graph-style retrieval had to work across thousands or millions of documents without graph-database performance degradation.
Multi-step tool-calling accuracy needs to approach 99-100% for business-critical processes.

Architecture

Writer presents a full-stack enterprise AI platform with open Palmera models, graph-based RAG implemented on Postgres rather than a graph database, Fusion Decoder retrieval, business-user no-code workflow definition, IT-owned code/deployment/monitoring interfaces, and self-evolving model research for multi-step agentic flows. Enterprises use the platform for workflow/action AI such as healthcare claims processing, portfolio-risk reports, and domain-specific business automation, with audit logs and customer-measured value used to build trust.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  business-workflow-designer["Business workflow designer"] --> it-code-and-deployment-layer["IT code and deployment layer"]
  it-code-and-deployment-layer --> graph-based-rag-on-postgres["Graph-based RAG on Postgres"]
  it-code-and-deployment-layer --> monitoring-and-audit-logs["Monitoring and audit logs"]
  graph-based-rag-on-postgres --> fusion-decoder-retrieval["Fusion Decoder retrieval"]
  graph-based-rag-on-postgres --> palmera-and-enterprise-models["Palmera and enterprise models"]
  fusion-decoder-retrieval --> palmera-and-enterprise-models
  palmera-and-enterprise-models --> workflow-action-runtime["Workflow action runtime"]
  palmera-and-enterprise-models --> self-evolving-feedback-loop["Self-evolving feedback loop"]
  workflow-action-runtime --> monitoring-and-audit-logs
  self-evolving-feedback-loop --> palmera-and-enterprise-models

Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component	Role	Interacts with
`Business workflow designer`	No-code interface where business users define workflows and domain processes.	`IT code and deployment layer`
`IT code and deployment layer`	Engineering surface for security, deployment, scaling, libraries, and monitoring.	`Graph-based RAG on Postgres`, `Monitoring and audit logs`
`Graph-based RAG on Postgres`	Retrieval layer that represents graph nodes and edges in PostgreSQL instead of a graph database.	`Fusion Decoder retrieval`, `Palmera and enterprise models`
`Fusion Decoder retrieval`	Dense-space retrieval/matching approach used instead of plain vector similarity.	`Palmera and enterprise models`
`Palmera and enterprise models`	Open-source and enterprise-controlled models used for workflow/action AI, function calling, and RAG responses.	`Workflow action runtime`, `Self-evolving feedback loop`
`Workflow action runtime`	Executes business workflows such as claims processing, portfolio-risk reporting, and other action-AI tasks.	`Monitoring and audit logs`
`Self-evolving feedback loop`	Model reflection and dynamic memory loop for improving repeated multi-step tool-calling tasks.	`Palmera and enterprise models`
`Monitoring and audit logs`	Visibility layer for inputs, outputs, performance, auditability, and customer-measured business value.	—

Data flow

Business users define workflows in no-code form and IT teams add code, libraries, deployment controls, and monitoring. Enterprise data is retrieved through Writer's graph-based RAG over Postgres with Fusion Decoder matching, passed to Palmera or other models for reasoning/action, and monitored through audit logs and tools such as LangSmith. For harder agentic flows, self-evolving models reflect on failures, update dynamic memory, and iterate toward better tool-calling behavior.

“Business users can describe workflow steps in natural language, which gets visualized as flows. IT can later add code, install libraries, and implement monitoring tools like LangSmith.”summary, stated

“The self-evolving model concept involves LLMs with active learning that continue training themselves in real-time without external databases or scripts.”summary, stated

Why this architecture

Writer's platform reflects enterprise pressure for owned, inspectable, and business-measurable AI. The architecture avoids opaque APIs, pairs business-process ownership with IT control, uses Postgres to make graph retrieval operationally scalable, and treats monitoring/auditability as part of the product rather than an optional dashboard.

“The build-versus-buy debate has evolved into a hybrid approach—enterprises want ownership and control without building everything from scratch or relying on opaque APIs from AI labs.”summary, stated

“The sweet spot appears to be letting business users define processes while IT maintains the underlying infrastructure.”summary, stated

Models used: Palmera · Fusion Decoder · PostgreSQL · LangSmith · LLMs · Self-evolving models

Scale

Writer describes deployments across Fortune 500 companies.
“deploying transformer-based systems at scale across Fortune 500 companies in financial services, healthcare, and consumer goods retail.”summary, stated
Healthcare claims workflows are described as involving hundreds of steps.
“Healthcare claims processing represents a major use case, where traditional workflows involve hundreds of steps.”summary, stated
Writer reports 30-50% workflow optimization in claims processing.
“AI systems can reportedly optimize these workflows by 30-50%, processing claims in real-time with significant time savings.”summary, stated
Successful enterprise customers are deploying hundreds of AI agents at scale.
“Writer reports that successful enterprise customers deploying hundreds of AI agents at scale are those where this partnership functions well.”summary, stated
Writer says each enterprise employee may supervise between two and five AI agents.
“On average, each enterprise employee may have between two and five AI agents, and they think of themselves as supervisors of these agents”summary, stated
The self-evolving model target is 99-100% accuracy for multi-step workflows.
“The goal is achieving 99-100% accuracy for multi-step workflows.”summary, stated

Alternatives considered (roads not taken)

Build graph-based RAG on a traditional graph database.

Rejected because: Writer found accuracy degradation and performance issues at enterprise document scale and represented nodes/edges in Postgres instead.

“they encountered significant challenges with traditional graph databases at enterprise scale. When dealing with thousands or millions of documents, standard approaches faced both accuracy degradation and performance issues.”summary, stated

Use simple vector database plus embedding retrieval.

Rejected because: Writer argues production RAG requires custom retrieval optimization, including Fusion Decoder and query translation.

“nobody today is just putting vector database with embedding and two lines of code. Each one has their own small tweak optimization around it.”summary, stated

Rely on public benchmarks like MMLU.

Rejected because: Enterprises lost trust in those benchmarks, so Writer emphasizes internal enterprise evaluations and business-value metrics.

“They report that enterprises have lost trust in standard benchmarks like MMLU due to data contamination concerns.”summary, stated

Use LLM-as-a-judge for Palmera evaluation.

Rejected because: Writer found self-judging unreliable and kept human involvement for evaluation.

“We try a lot to use LLM as a judge. It did not end well. Make Palmera to judge Palmera—yes, always everything is great.”summary, stated

Patterns

`rag-retrieval` (retrieval)

Writer implements graph-style RAG using Postgres-backed nodes/edges and Fusion Decoder retrieval instead of a graph database.

“they built what they call a "graph-based system" without using any graph database, instead leveraging Postgres.”summary, stated

`tool-use-orchestration` (workflow automation)

The platform supports workflow/action AI that runs business processes and multi-step tool-calling flows rather than only chat.

“The dominant pattern is now "workflow and action AI"—systems that optimize complex business processes.”summary, stated

`fine-tuning-adaptation` (model ownership)

Writer builds and open-sources Palmera models for enterprise-controlled deployments rather than relying only on external proprietary APIs.

“Writer has made their Palmera models open source for enterprise use in response to this demand.”summary, stated

`observability-tracing` (operations)

Enterprise deployments require visibility into inputs, outputs, processing details, audit logs, and monitoring tools.

“enterprises require full visibility into inputs, outputs, and all processing details to maintain performance and guarantee results.”summary, stated

`agent-memory` (agent reliability)

Self-evolving models build dynamic memory from reflection over repeated task failures to avoid repeating mistakes.

“The model takes feedback, builds dynamic memory, and improves over time.”summary, stated

Tradeoffs

Implement graph-style retrieval on Postgres.

Chose PostgreSQL representation of graph nodes and edges over Traditional graph databases.

Rationale: Postgres avoided the enterprise-scale accuracy and performance problems Writer encountered with graph databases.

“The rationale is that graphs are fundamentally nodes and edges, which can be efficiently represented in PostgreSQL.”summary, stated

Use a business-plus-IT operating model.

Chose No-code workflow definition for business users plus code/deployment control for IT over A purely IT-built or purely business-owned agent system.

Rationale: Business users own workflow knowledge while IT owns security, deployment, scaling, and monitoring.

“IT teams maintain responsibility for security, deployment, and scaling, but business users own the actual workflows and understand the domain-specific processes.”summary, stated

Prioritize enterprise-owned/open models.

Chose Open-source Palmera and owned deployments over Opaque proprietary model APIs.

Rationale: Large enterprises view AI as core infrastructure and want control rather than renting black-box services.

“"We're done with the renting, I'm done with the black box, I'm done with this lab giving me an amazing model, and six months later, distill the API and everything basically blows up."”summary, stated

Measure business value instead of optimizing public benchmarks.

Chose Time savings, productivity gains, use-case metrics, and internal enterprise evals over MMLU-style benchmark chasing.

Rationale: Enterprise buyers trust business-measured results more than contaminated public benchmarks.

“Instead, they focus on business value metrics—specifically, time savings and productivity gains.”summary, stated

Gotchas (what bit them)

Graph databases did not scale cleanly for enterprise RAG.

Root cause: At thousands or millions of documents, traditional graph-database approaches had performance and accuracy issues.

Resolution: Represent graph nodes and edges in PostgreSQL and tune retrieval with Fusion Decoder.

“When dealing with thousands or millions of documents, standard approaches faced both accuracy degradation and performance issues.”summary, stated

Question translation mattered more than retrieval-parameter tuning.

Root cause: Enterprise users ask in radically different styles, from terse keyword searches to vague context-heavy questions.

Resolution: Optimize query translation and behavior handling rather than only tuning embedding or graph parameters.

“the critical optimization isn't in the data, graph, or matching algorithms—it's in question translation.”summary, stated

LLM-as-judge failed for self-evaluation.

Root cause: A model judging itself inflated quality judgments.

Resolution: Use human involvement and enterprise-specific evaluation rather than self-judging the same model.

“Make Palmera to judge Palmera—yes, always everything is great.”summary, stated

Eighty to ninety percent accuracy is not enough for business-critical agents.

Root cause: Multi-step tool-calling errors become unacceptable when even 10 of 100 requests can be completely wrong.

Resolution: Pursue self-evolving/reflection-based models aimed at 99-100% task accuracy.

“You tell me every 100 requests, 10 of them are completely wrong—I cannot run a business with this.”summary, stated

Evaluation

Writer evaluates with internal enterprise-focused tests for function calling and hallucination, customer-measured productivity/time-savings metrics, human involvement, and use-case-specific business value rather than public benchmark scores alone.

Metrics: Time savings · Productivity gains · Average time to process claims with and without AI · Function-calling capability · Hallucination measurement · Business value by use case

“For their own model evaluation (the Palmera series), they've created in-house internal evaluations rather than relying on public benchmarks.”summary, stated

“In healthcare claims processing, they measure average time to process claims with and without AI.”summary, stated

Outcomes

Writer reports enterprise workflow optimization of 30-50% in healthcare claims processing.
The platform supports enterprises deploying hundreds of agents at scale.
The company is pursuing self-evolving models for production-grade multi-step tool-calling reliability.

The claims are from a vendor conversation and should be treated as high-bias evidence until independently verified.

“AI systems can reportedly optimize these workflows by 30-50%, processing claims in real-time with significant time savings.”summary, stated

“The primary use case is agentic flows with multi-turn, multi-step tool calling, where traditional approaches often drop to 20-30% accuracy.”summary, stated