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AgentDrops

Services

Agents, voice and software. Proven before they’re built.

Five kinds of work, one method behind all of them: experiment, measure, then build what the evidence supports.

01

Agentic products

AI agents that take a goal, use your tools and data, and complete real work. Not chatbots with ambitions.

  • Multi-step agents for operations, support, sales, finance and internal workflows
  • Tool use and integrations with your CRM, ERP, ticketing, databases and APIs
  • Multi-agent orchestration for work that spans teams and systems
  • Guardrails, permission scopes, human approval and kill switches by default
  • Evaluation suites that prove an agent does the job before it gets write access

02

Voice agents

Voice agents that answer, qualify, book and follow up, and know when to hand the call to a person.

  • Inbound and outbound calling: support, booking, qualification, reminders
  • End-to-end latency designed and measured against natural turn-taking
  • Interruption handling, end-of-turn detection and graceful hand-off
  • Multilingual and accent-aware conversations
  • Telephony and contact-centre integration, call analytics and QA

People answer each other in under about 300 ms. We measure our voice agents against that bar, not against a demo.

03

Custom software

Web platforms, internal tools, mobile apps and APIs, built on modern typed stacks your team can own.

  • Web platforms, internal tools, dashboards and portals
  • Mobile apps
  • APIs, data pipelines and integrations
  • AI features embedded into products you already run
  • Typed, tested and documented, built to be maintained by your team

04

Applied research

Feasibility sprints, model evaluations and prototypes for teams that need evidence before a budget.

  • Feasibility sprints: can this be done with today’s models, at what quality and cost?
  • Model and vendor evaluation on your data and your tasks
  • Evaluation design: the test sets and success metrics your system will be judged by
  • Prototypes, with a written report of what worked, what didn’t and why

05

AI automation

Workflow automation, document AI and retrieval over your own knowledge, with monitoring once it is live.

  • Workflow automation across the tools you already use
  • Document processing, extraction and classification
  • Retrieval (RAG) over your knowledge base, built for accuracy and speed
  • Monitoring and observability for AI systems already in production

Who we work with

  • Startups

    Turning an AI idea into a product people pay for.

  • Growing businesses

    Automating operations, support and sales.

  • Enterprises

    Agents and voice systems that pass security and compliance review.

  • Product teams

    Adding AI features to software they already run.

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.

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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