Amine Hn
Co-founder · PhaseLink
Co-founder at PhaseLink. Builds AI agents in production for SMBs and mid-market — voice, chatbots, automation. Vendor-neutral by reflex.
Amine co-founded PhaseLink to fix what most AI consultancies get wrong: shipping pilots that never reach production. He spends most of his time on the audit step — the unglamorous work of figuring out which operations actually pay back when automated and which ones should be redesigned instead.
Before PhaseLink, Amine spent years building software for businesses operating at the awkward middle — large enough that manual processes hurt, small enough that off-the-shelf SaaS doesn't fit. That gap is where AI automation is most useful, and it's the audience PhaseLink is built around.
He writes for the PhaseLink guides and blog on AI implementation, voice agents, chatbots, and the operational discipline that determines whether a project ships or stalls.
- AI implementation
- Voice agents
- Process automation
- AI consulting
- Eval design
- →AI implementation playbooks
- →Vendor-neutral architecture decisions
- →Voice agent latency tuning
- →Production handoff design
- ~13 min read
The complete guide to AI automation for businesses in 2026
Where AI automation actually pays back, how to scope a workflow that ships, and the operational traps that quietly kill most automation projects in their second quarter. Vendor-neutral. Audit-first. Built from the engagements we've actually run.
read guide→ - ~12 min read
AI voice agents in 2026: build, deploy, measure
What AI voice agents actually do well, the latency and tooling decisions that determine whether the call sounds like a person, and the failure modes you need to design around before you put one in front of a customer. Built from inbound and outbound voice agents we've shipped to production.
read guide→ - ~12 min read
AI chatbots for customer support and sales in 2026
What separates a chatbot that deflects 60% of tickets at 4.6/5 CSAT from a chatbot that gets turned off after six weeks. The architecture, the failure modes, and how to size the build so it earns its place.
read guide→ - ~11 min read
Process automation with AI: frameworks, tools, and what to ship first
How to identify the operations in your business worth automating, the modern stack (n8n, Make, custom code, LLMs), and the decision rules that determine whether AI belongs in the workflow or whether deterministic code would do the job better and cheaper.
read guide→ - ~14 min read
How to implement AI in your business in 2026
An audit-first, vendor-neutral playbook for shipping AI to production in 2026. What to build, what to skip, how to measure it — and the implementation traps that quietly kill most pilots at month four.
read guide→
- May 24
How to read AI model benchmarks in 2026 (and what to ignore)
The benchmark scores you see in launch posts and vendor comparisons are mostly noise for production decisions. Here's what the numbers actually mean, which benchmarks track real-world performance, and what to test on your own data instead.
- May 23
The hidden cost of model-agnostic AI architecture
Model-agnostic architectures sound prudent and future-proof, and we recommend them most of the time. But there's a hidden cost most teams don't price in until they're a year deep. Here's what to know.
- May 22
When NOT to build an AI chatbot (and what to build instead)
Chatbots are the default AI build for businesses, and the default is often wrong. Here's when a chatbot is the right call, when it isn't, and what the better alternative looks like.
- May 21
Five patterns we see in successful AI implementations
The teams that ship AI to production and keep it there share a small number of predictable patterns. None of them are about the model. Field notes from the engagements we've run.
- May 20
Why most AI POCs die between month two and month four
About 1 in 4 AI proofs-of-concept never make production. The failure mode is almost always operational, not technical — and it's predictable enough to design around. Here's what kills them.
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