When we started Alovalabs, the most obvious path was to build a general AI assistant. The market was there. The infrastructure existed. The pitch writes itself: 'your AI co-pilot for everything'. We deliberately chose not to.
The Problem with General-Purpose
General-purpose AI tools are impressive and genuinely useful — but they optimise for breadth. They can help with a hundred things reasonably well. What they rarely do is solve one specific, high-friction workflow exceptionally well. That's where the real value sits.
High-friction workflows are those where expertise, speed, and consistency all matter simultaneously. Technical hiring screening. Resume optimisation against a specific role. AI brand visibility monitoring. These aren't tasks where 'good enough' is acceptable — the stakes are high and the details matter.
Depth Over Breadth
A surgical tool is designed around the full context of its workflow. It understands the upstream and downstream of the task. A technical interview tool knows what a good coding question looks like, what anti-cheat signals to watch for, and what a hiring manager needs in a report. A general chatbot knows none of this implicitly — you have to reconstruct that context every single time.
- Surgical tools have workflow-aware defaults — no prompt engineering required from the user
- They produce outputs in the formats the workflow actually demands
- They can be evaluated against clear quality benchmarks
- They build institutional knowledge into the product, not just the prompt
The Business Case
Focused tools are also more defensible. A general AI assistant can be replicated by OpenAI or Anthropic overnight — they have the model, the distribution, and the brand. A tool deeply embedded in a hiring workflow, with proprietary evaluation logic, integrations, and months of tuning, is a different story.
“We're not building the Swiss Army knife. We're building the scalpel. And in surgery, you always want the scalpel.”
— Akash Srivastava
What This Means for Alovalabs
Every product we build at Alovalabs starts with a specific, measurable problem in a specific workflow. We ask: where is the most time lost? Where is the most value destroyed by inconsistency or manual effort? Then we build a focused tool that solves exactly that, nothing more. This is slower than shipping a general assistant. It's also the only way to build something people actually depend on.