Automate Project Allocation and Resource Planning with AI

Many professional services firms manage staffing with a master spreadsheet. This works for a handful of people, but breaks down as the team grows. The operations manager can filter by role, but cannot query for nuanced combinations like, "Who is our best available Python developer with financial services experience who is not on vacation next month?" The spreadsheet doesn't connect to calendars or time tracking tools, so availability data is always stale.

Off-the-shelf Professional Services Automation (PSA) tools like Kantata or Accelo are often too rigid for firms under 50 people. Their data models are fixed. If you want to track a new, critical skill like "Claude API expertise," you cannot add it as a first-class searchable field. They are designed for large-scale time and billing, not for nuanced, high-stakes talent allocation. The result is that the most critical staffing decisions still happen outside the tool, back in spreadsheets and email chains.

The core architectural issue is that these tools treat people as interchangeable blocks of time. They cannot model the complex, multi-dimensional nature of human skills, certifications, client history, and personal development goals. A spreadsheet cannot query a calendar, and a generic PSA tool cannot parse unstructured text in a resume to find a niche skill. They are designed as databases, not as intelligent decision engines that can recommend the optimal person for a job.

This manual process directly impacts profitability. Every hour a partner spends juggling spreadsheets is an unbillable hour. Every day a consultant sits on the bench because their skills were not visible to the right person is lost revenue. A poor staffing choice can lead to project delays or client dissatisfaction, which has long-term consequences beyond a single project's margin.

The first step is a data audit. Syntora would connect to your existing systems: HR platforms, time tracking software, and any skills matrices you maintain. The goal is to build a single, unified data model of your consultants, including hard skills, certifications, project history, and real-time availability. This audit produces a data map and a clear plan for integration. You see exactly what data is usable before any build work begins.

The technical approach would center on a FastAPI service that uses a Claude API pipeline. This pipeline would parse unstructured data, like text from resumes or past project descriptions, and convert it into a structured skills vector stored in a Supabase database. When a new project request is entered, the system converts its requirements into a similar vector. A similarity search then finds the top 5 best-matched available consultants in under 500ms. We use Python for this because its data science libraries handle the vector math efficiently.

The delivered system would be a simple internal web application where a manager can input new project requirements and see a ranked list of available, qualified staff. The application would connect to your firm's calendar and time tracking systems to ensure availability is always current. You receive the full source code on your GitHub, the Supabase database schema, and a runbook for maintenance. This entire system can run for under $50 per month on Vercel and AWS Lambda, processing over 100 requests per day.