Build a Custom AI System for SOW Automation

Many IT consulting firms rely on proposal tools like PandaDoc or Proposify. These platforms standardize formatting but do not generate the actual content. A consultant still manually writes the scope, deliverables, and pricing from discovery notes, turning the tool into a glorified PDF generator. The core bottleneck of interpreting client needs and structuring the project plan remains untouched.

Others use the quoting features in HubSpot or QuickBooks. These are designed for line-item sales, not complex professional services engagements. They can pull service descriptions and rates, but they cannot draft a nuanced 'Project Approach' section or list critical 'Client Responsibilities'. This forces consultants to write the narrative sections in a separate document, then copy-paste everything together, creating version control issues and risking errors.

For example, a senior architect at a 50-person firm finishes a discovery call for a complex cloud migration. They have a dozen pages of unstructured notes in OneNote. The process involves finding a similar SOW from a past project, manually updating the scope, calculating engineer-hours for pricing, and getting partner review. This workflow takes at least 4 hours of a highly paid employee’s time, delays getting the proposal to the client, and risks including outdated service terms from the old SOW.

The structural problem is that these tools are built for static templating, not dynamic synthesis. They lack the architecture to ingest unstructured data like call transcripts and generate a coherent, logically-structured document. A custom system is required to bridge the gap between raw client input and a polished, accurate Statement of Work.

The first step would be a data audit. Syntora would analyze a sample of 100-200 of your past SOWs, corresponding discovery notes, and email threads. This audit maps the structure of your proposals, identifies common service components, and defines the data patterns required to train the AI. You would receive a data readiness report that confirms the project's feasibility and outlines any required data standardization before the build begins.

The core of the system would be a Python-based FastAPI service running on AWS Lambda for scalable, on-demand processing. We would use the Claude API via AWS Bedrock, which keeps your data within a secure AWS environment and prevents it from being used for model training. A Supabase PostgreSQL database would store vectorized embeddings of your past SOW content, creating a searchable knowledge base. When a new request comes in, the system finds the most relevant historical context to inform the new SOW draft.

The delivered system integrates directly into your existing workflow. A consultant can upload call notes or a transcript through a simple interface or directly from a HubSpot deal record. The FastAPI service orchestrates a prompt chain that references the vector database and generates a complete SOW draft in DOCX format. This draft, which includes scope, deliverables, assumptions, and pricing, is then attached back to the HubSpot deal, ready for a 15-minute human review.