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AgentDrops

Research-first AI company

We experiment first. Then we drop it for you.

AgentDrops builds agentic products, voice agents and custom software. Every one starts as an experiment in our lab, tested against real conditions, before a line of production code is written for you.

Start with an experiment See how the lab works
  1. 01 Question
  2. 02 Experiment
  3. 03 Evidence
  4. 04 Build
  5. 05 Drop
  6. 06 Measure
Every idea passes the same six steps before it ships.

Why research first

AI has a production problem, not a model problem.

Most AI projects go straight from a demo to a deadline. The step in between, the one that answers whether it works, is the step that gets skipped.

of enterprise AI proofs of concept never reach wide deployment.
88%
Source: IDC with Lenovo, via CIO.com, 2025
of agentic AI projects are expected to be canceled by the end of 2027.
40%+
Source: Gartner, 2025

None of those failures is about the model being too weak. They come from questions nobody asked first: what does success look like, where does it break, what does each task cost, and what happens when it goes wrong. We answer those before we build. That is the whole company.

What we do

We build agents, voice and software. We prove them first.

Five kinds of work, one method behind all of them.

The lab

Every build starts as a written experiment.

Before we test anything, we write down the question, the success line and the rule for stopping. Then we run it on real cases and report what happened, including when it fails.

  • Every experiment states its question and success line before it runs.
  • Every experiment has a kill rule, and we follow it.
  • Failed experiments are recorded and reported, not hidden.
  • Results are measured on real cases, not on hand-picked examples.
Inside the lab
EXP-000 Example record

Voice booking agent: turn latency

Question
Can a voice agent book appointments without callers noticing a delay?
Success
p95 end-to-end turn latency under 800 ms; booking completed without a human
Variables
Speech models, LLM, streaming strategy, end-of-turn detection
Test set
Recorded real-world calls, including interruptions and background noise
Kill rule
If the target is missed on every configuration, we stop and report
Drop only if the success line holds

How we work

From lab to drop, in six steps.

Every project follows the same path. If the experiments say an idea will not work, you hear it at step three, before you have paid for a build.

  1. 01

    Question

    We define the job, the user and what success means, in numbers.

    → A one-page brief with success criteria

  2. 02

    Experiment

    We test models, prompts, tools and architectures against real cases and edge cases.

    → An experiment log of what we tried and measured

  3. 03 Go / no-go

    Evidence

    We show you accuracy, latency, cost per task and failure modes.

    → A go or no-go decision, made on data

  4. 04

    Build

    We build the production version on the approach the evidence picked.

    → Working software, tested and reviewed

  5. 05

    Drop

    We release it into your business with monitoring, guardrails and a rollback plan.

    → A live product, not a pilot

  6. 06

    Measure

    We track it against the criteria from step one and keep improving it.

    → Ongoing reports and iterations

A fast “no” is a result too.

Why AgentDrops

What you get that a demo doesn’t give you.

  • Research is the first deliverable

    You see the experiments, not just the demo. The log is yours to keep.

  • Success criteria before code

    Every project starts with how it will be measured, agreed in writing.

  • Built for production, not pilots

    We design for the small share of AI projects that ship, from day one.

  • Safe by default

    Least-privilege access, human approval on risky actions, audit logs and kill switches are part of the build.

  • Model-agnostic

    We use the model the evidence picks, and switch when a better one arrives.

  • You own everything

    Code, prompts, evaluation sets and documentation belong to you.

Questions

Asked before every project.

What does “research-first” mean in practice?

Before we build anything, we run short experiments on your real cases: which models work, how fast, at what cost and where they fail. You get the results and a go or no-go recommendation before any production build starts.

What if the experiments say it won’t work?

Then we tell you, with the data, and you have spent a fraction of a build budget to find out. A fast “no” saves months.

Which models and platforms do you use?

Whichever the evidence picks for your task. We are model-agnostic and work across the major model providers and open models, and we design so you can switch later.

Who owns the code and the data?

You do. Code, prompts, evaluation sets and documentation are handed over, and your data is only used for your project.

How do you keep agents safe?

Least-privilege access, human approval for risky actions, audit logs, rate limits and a kill switch are part of every agent we ship, not an add-on.

Can you work with our existing systems?

Yes. Most of our work connects to tools you already run: CRMs, ERPs, ticketing, telephony, databases and internal APIs.

Bring us the idea. We’ll bring the evidence.

Tell us what you want to build. We will tell you how we would test it, and what a first experiment would show.

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