AllBots Resources
10 places to go depending on what you are trying to decide. Research and customer evidence if you are still evaluating whether an AI voice agent fits your operation. Developer and API documentation if the decision is made and someone now has to integrate it. Process, training, partner and press material if you want to know how an engagement runs and who runs it.
Nothing here is gated behind a form. We would rather you arrive at a scoping call having already read the parts that apply to you, because the conversation is far more useful when it starts from your call volumes and your systems instead of a generic pitch. If you are not sure where to begin: read one case study from an industry close to yours, then the process page. Those two together answer most first questions.
Research and evidence
Start here if you are still deciding whether a voice agent belongs in your operation, and what it realistically changes.
Long-form research on how conversational AI behaves once it is answering real phone calls: where deflection actually comes from, what a caller will and will not tolerate from a machine, and how to measure containment without flattering the numbers.
Written for the person who has to defend the decision internally. Each paper states its assumptions, so you can substitute your own call volumes and staffing costs rather than inheriting ours.
Named deployments with the before-and-after: what the phone queue looked like, which calls the agent took over first, what stayed with the human team, and how the workflow behind the call changed.
These are the fastest way to find a build that resembles yours. If your situation matches one of them closely, the scoping call gets much shorter.
Shorter, more frequent writing: implementation notes, prompt and conversation-design patterns, integration gotchas, and plain explanations of the parts of the stack that vendors usually leave vague.
This is where a change in how we build shows up first, well before it reaches a whitepaper.
Build and integrate
For engineers who need to wire an agent into a CRM, a scheduler, or an existing telephony estate.
The engineering entry point: how an AllBots agent is structured, how webhooks and events flow out of a live call, and how to run an agent against a staging environment before it ever touches a real caller.
Includes the integration patterns we reach for most often, and the ones we advise against.
Endpoint-level reference for agents, calls, transcripts, and phone-number provisioning, with authentication, error shapes, and rate limits spelled out.
Use it alongside the developer hub: the hub explains the model, the reference gives you the exact request and response.
Definitions for the vocabulary that surrounds this category: containment, barge-in, ASR and TTS, turn latency, warm transfer, knowledge base, and the rest.
Handy when a proposal or a vendor comparison uses a term as though everyone agrees on what it means.
Working with AllBots
How an engagement actually runs, who it runs with, and what your team learns along the way.
The build sequence end to end: discovery of the calls you handle today, conversation design, integration, testing against recorded and live traffic, launch, then tuning once real callers arrive.
It also names what we need from you at each stage, which is usually the thing that determines whether a project lands in two weeks or six.
Enablement for the people who own the agent after launch: reading transcripts, adjusting a flow, updating a knowledge base, and knowing when an escalation rule needs changing rather than a prompt.
The goal is an internal owner who does not need us for routine changes.
For agencies, MSPs, and consultancies whose clients keep asking for voice automation. Covers how we split delivery, what you can white-label, and the commercial terms.
If you already own the client relationship, this is usually a faster route than rebuilding the capability yourself.
Company facts, brand assets, leadership background, and a media contact, plus the announcements worth citing.
Journalists and analysts should be able to write about us accurately from this page alone.
How we keep this current
The library changes because the work does. New case studies get published as deployments mature enough to say something honest about them. API documentation is updated with the platform rather than after it. The glossary grows every time a term turns up in a client conversation with three different meanings attached to it. When something material changes in how we build, it appears on the blog first and works its way into the process and training pages once it has held up across more than one project.
If something you need is missing, or a page here answers a slightly different question than the one you actually have, tell us. Gaps in this library are usually gaps in how we explain the product, and those are worth fixing.
Read enough?
Bring your call volumes and the systems you already run. We will scope what an agent would take over first, and tell you plainly if it is not worth doing yet.