Build Custom AI Agents for Your Firm's Operations
Syntora builds AI agents to automate proposal generation and project reporting for professional services firms. We connect your CRM, time tracking, and accounting software to create a unified data source for operations.
Key Takeaways
- Syntora builds AI agents to automate internal workflows like proposal generation and project reporting for professional services firms.
- The system connects disparate tools like HubSpot, QuickBooks, and time tracking software into a unified operational view.
- We use the Claude API to parse unstructured data from past SOWs and project notes to inform new proposals.
- A typical automation reduces the time to generate a complex proposal from over 3 hours to under 5 minutes.
Syntora designs AI agents for professional services firms to automate internal operations. These systems connect HubSpot, QuickBooks, and time tracking data to generate proposals in under 5 minutes. The architecture uses a FastAPI service and the Claude API, running on a Supabase backend.
The complexity depends on your specific tools and business logic. A firm using HubSpot and QuickBooks with a clear SOW template is a 4-week build. A firm with custom project phases and multiple time-tracking systems requires more upfront data mapping.
The Problem
Why Does Generating Proposals for Professional Services Still Involve So Much Manual Work?
Most professional services firms run on a collection of best-in-class tools: HubSpot for CRM, QuickBooks for accounting, and a dedicated tool like Harvest for time tracking. While each is powerful on its own, their native integrations are shallow. They sync contact records but fail to connect the critical data that drives profitability: time entries, project costs, and deal specifics.
Consider a 15-person consulting firm. To create a new proposal, a partner opens three browser tabs. They pull past project invoices from QuickBooks to estimate costs, analyze hours on similar tasks in Harvest, and check the client's history in HubSpot. This data is manually copied into a Word document, a process taking 2-3 hours of expensive time. An error in this manual transcription can lead to a project being underpriced by thousands of dollars.
The structural problem is that these are separate systems of record with different data models. HubSpot's 'Deal' object has no native concept of 'Billable Hours' from Harvest or 'Invoice Status' from QuickBooks. Off-the-shelf connectors cannot bridge this contextual gap because they lack the business logic to map a specific time entry category to a specific line item on a new Statement of Work.
This manual reconciliation isn't just a time sink. The slow, manual process results in inconsistent pricing, delayed proposals that lose to faster competitors, and a constant risk of under-scoping projects, directly impacting the firm's profitability.
Our Approach
How Syntora Architects an AI Agent for Proposal and SOW Automation
The engagement would begin with a data flow audit. Syntora maps how information moves from a new lead in HubSpot to a final invoice in QuickBooks. This process identifies the key data points and business rules needed for accurate proposal generation. The deliverable from this phase is a clear architectural diagram and a plan for creating a centralized 'project truth' database using Supabase.
The core of the system would be a Python-based FastAPI service that listens for a webhook from HubSpot when a deal reaches the 'Proposal' stage. This service triggers parallel queries to QuickBooks for historical project costs and Harvest for relevant time data for similar projects. The Claude API then parses this structured data, along with text from past successful SOWs, to draft a new, context-aware proposal that matches your firm's template and tone. We choose FastAPI for its async capabilities, ensuring all external API calls complete in under 500ms.
The delivered system provides a simple interface for a partner to review, edit, and approve the AI-generated draft. Your team's process for generating a data-backed SOW would drop from hours to under 5 minutes. You receive the full source code in your own repository, a detailed runbook, and a system deployed on AWS Lambda that typically costs under $50 per month to operate.
| Manual Proposal Process | Syntora's Automated Workflow |
|---|---|
| Time to Generate SOW | 2-4 hours of partner-level work |
| Data Sources Used | Manually checked across 3+ systems |
| Risk of Scoping Error | High, based on manual data entry |
Why It Matters
Key Benefits
One Engineer, No Handoffs
The person on the discovery call is the engineer who writes every line of production code. No project managers, no communication gaps between sales and development.
You Own Everything
You receive the full Python source code in your GitHub repository, plus a runbook for maintenance and future updates. There is no vendor lock-in.
Realistic 4-Week Timeline
A typical proposal automation system for a professional services firm is scoped, built, and deployed in four weeks. This timeline is confirmed after the initial data audit.
Transparent Post-Launch Support
After launch, Syntora offers an optional flat-rate monthly retainer for monitoring, maintenance, and system updates. You know the exact costs upfront with no surprises.
Professional Services Focus
Syntora understands the critical link between time tracking categories, SOW line items, and final invoicing. The system is built around your firm's specific business logic, not generic templates.
How We Deliver
The Process
Discovery Call
A 30-minute call to map your current workflow, key software tools, and business objectives. You receive a detailed scope document within 48 hours outlining the approach and a fixed-price quote.
Data & API Audit
You provide read-only access to your CRM, accounting, and time tracking systems. Syntora confirms data quality and presents the final technical architecture for your approval before any code is written.
Build & Weekly Demos
The system is built over a 2-3 week period with weekly live demos. You provide direct feedback on the generated outputs, allowing for rapid refinement of the AI's logic and accuracy.
Handoff & Training
You receive the full source code, a deployment runbook, and a one-hour training session for your team. Syntora actively monitors the system for four weeks post-launch to ensure stability.
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The Syntora Advantage
Not all AI partners are built the same.
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Assessment phase is often skipped or abbreviated
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We assess your business before we build anything
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Typically built on shared, third-party platforms
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Fully private systems. Your data never leaves your environment
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May require new software purchases or migrations
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Zero disruption to your existing tools and workflows
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Training and ongoing support are usually extra
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Full training included. Your team hits the ground running from day one
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Code and data often stay on the vendor's platform
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You own everything we build. The systems, the data, all of it. No lock-in
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