Commercial Operations Platform
Uses Historical Jobs and Structured Templates to Improve Quote and Compliance Workflows

The business was producing complex operational quotes and compliance documents in a category where quality depends heavily on historical context, repeatable rules, and clear document structure.

One of the most persistent problems was inconsistency. Different staff members would approach the same job type and arrive at quotes that varied significantly in scope, structure, and pricing assumptions. There was no single source of truth to anchor the quoting process — experienced team members carried the institutional knowledge in their heads, and that knowledge was not transferable in any reliable way.

New staff had no reference point. Without access to prior work, they were effectively starting from scratch on every quote, making decisions based on incomplete context and producing outputs that looked nothing like what the business had delivered before. The cost of a wrong assumption was not just a rework cycle — it was a compliance risk and a margin risk on every affected job.

Generic AI output was not enough. The workflow needed to reference prior jobs, reflect domain-specific constraints, and generate outputs that looked consistent with how the business already operated. The challenge was to combine retrieval, structured form inputs, and document rules into a process that was useful in production rather than just interesting in demos.

We built a quote-generation workflow that combines structured user inputs with retrieval over a historical job library, then uses those matched examples to guide generation.

The decision to use retrieval-first rather than pure AI generation was deliberate. A model generating from scratch has no obligation to reflect how this business actually prices and scopes work. It produces plausible-sounding output, but plausible is not the same as correct. Retrieval changes the starting position — instead of an empty context, generation begins with real examples drawn from the 368 historical jobs already in the system. That makes outputs auditable and grounded in operational reality rather than statistical approximation.

The 368 jobs were indexed using vector embeddings, allowing the workflow to retrieve the closest matching precedents based on job type, scope, and context rather than exact keyword matches. Each query surfaces the five most relevant examples, giving the generation step anchored, evidence-based input to work from.

Technology Stack

• Vector search: Historical jobs are indexed and retrieved by similarity so generation starts from relevant operational precedent.
• Workflow engine: Structured user inputs are turned into consistent search and generation requests instead of free-form prompts.
• Document builder: Compliance-oriented outputs are assembled through template logic rather than uncontrolled long-form generation.
• Operations platform: Quote and document workflows live inside the same user environment for review, overrides, and finalisation.
• Override controls: Users can inspect matched context, adjust calculated values, and guide the final output before completion.

Quotes are now grounded in actual historical data regardless of who generates them. That shift — from individual memory to shared operational precedent — is the most significant outcome of the project.

A new team member working through the workflow arrives at the same reference material as an experienced one. The 368 indexed jobs surface relevant examples automatically, removing the dependency on any individual’s recall. Outputs are traceable back to specific historical work, which strengthens internal confidence and supports compliance review when required.

Instead of beginning from a blank page, teams work from historically similar jobs, structured prompts, and predefined document logic. The consistency improvement is not cosmetic — it reflects the business’s actual pricing and scoping patterns rather than an approximation of them.