Every person at CoVector AI — regardless of role — spends roughly 20% of their time on R&D. Reading papers. Running experiments. Benchmarking new models. Testing techniques we haven't used in client work yet.
This isn't a Google-style "20% time" perk. It's a deliberate business decision, and here's why.
The Problem We're Solving
AI consulting has a shelf-life problem. The techniques that delivered results six months ago may be obsolete today. If your team only works on client deliverables, their knowledge decays in real time.
We've seen this pattern at other firms: consultants deploy the same stack for every client because it's what they know, not because it's what's best. The client gets yesterday's solution at today's prices.
How It Works
Structured, not free-form. Our R&D time isn't "do whatever you want." Each person maintains a research log with:
- What they're exploring and why
- Experiment results and findings
- Practical implications for client work
Shared, not siloed. Weekly briefs where the team shares what they've learned. The format is: "Here's what I tested, here's what it means for us, here's when we'd use it."
Applied, not academic. We're not writing papers. The question is always: "Could this improve something we're building for a client?" If the answer is "interesting but not useful yet," we note it and move on.
What It Produces
In the past quarter, R&D time directly led to:
- Switching a client's document pipeline from a custom model to a new multimodal approach that cut costs 60%
- Discovering an evaluation framework that caught quality issues our previous approach missed
- Building an internal tool that reduced our own proposal creation time from 2 days to 3 hours
None of these would have happened if we were 100% focused on delivery.
The Business Case
For clients: You're getting a team that's genuinely current. When we recommend an approach, it's because we've tested alternatives last week, not because we read about it last year.
For hiring: R&D time is a meaningful differentiator when recruiting. People who care about staying sharp — the ones you want on your team — value the space to learn.
For the firm: Every insight from R&D is potential IP. Internal tools, evaluation frameworks, deployment patterns — these compound over time into reusable assets.
The Trade-Off
20% R&D means 80% delivery. We're explicit about this with clients: our engagements are scoped for 80% utilisation, not 100%. The remaining time makes the 80% significantly more effective.
Some firms bill 100% and learn on the job. We'd rather be honest about the allocation and deliver better results.
Why It Matters for Our Clients
When you work with CoVector AI, you're not just getting consultants who know today's AI. You're getting a team that's actively preparing for what's next. In a field that moves this fast, that's the difference between a vendor and a partner.
