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The work your team repeats every week, running without them.

Agents that do the work, and stop at a person when it matters.

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  1. Work arrives

    EmailWeb formPhone call

  2. The agent reads, decides, acts and records

    It works the request the way your team would, one step at a time.

    step budget · tool allowlist

  3. A person approves

    Anything that moves money or changes an identity waits for a human.

  4. Your systems are updated

    CRMBillingDocs

  5. The run is logged

    Every step it took, kept so you can check it later.

Work in, work done, every step recorded.
  • Fixed price per phase
  • You own the code
  • 2–4 week average launch

What changes

What this takes off your week.

Four patterns account for most of what we are asked to automate.

Someone triages the inbox every morning.
An agent triages it, and escalates what matters.
One person remembers how the process runs.
The process runs itself, the same way, every time.
Answers are buried in documents.
The documents answer, and cite the paragraph.
After-hours calls go to voicemail.
A voice agent answers, and books the appointment.

What we build here

Seven things live under AI and automation.

They share one engineering standard and differ only in what they touch.

  1. AI agents

    An agent reads each request, acts through your own tools, and stops at a person before anything irreversible.

    Scope an agent for one workflow →

  2. Workflow automation

    Work triggers itself, retries safely when a step fails, and routes to a person wherever money moves.

    Automate a process end to end →

  3. Voice agents

    A voice agent answers the call, books or resolves it, and hands the live transcript to whoever picks up next.

    Answer the calls you are missing →

  4. RAG systems

    Your documents become answerable, with the source paragraph attached to every answer so it can be checked.

    Make your documents answerable →

  5. Chatbots

    A chatbot answers from your own content, refuses what it does not know, and passes the hard questions to a person.

    Ground a chatbot in your own content →

  6. AI integrations

    Model calls sit behind your own service boundary, with budgets and timeouts, so changing provider is never a rewrite.

    Put AI behind your own API →

  7. AI consulting

    A short scoping engagement that ends with a specification, an architecture, and a fixed price for each phase.

    Scope it before you commit →

How it fails safely

The parts that decide whether a pilot survives production.

A demo is easy to build and a Tuesday is not. Every control below is checkable after handover.

  1. A run starts

    One job, triggered by a message or a schedule.

  2. Tool allowlist

    approved tools only

  3. Step budget

    hard ceiling

  4. Human gate

    Anything touching money or identity stops here and waits for a person.

    money · identity

  5. Idempotency

    no double writes

  6. Evals

    failing blocks deploy

  7. Run log

    Every step it took, in order, kept after the fact.

    append only

Six controls, in order. Each one is checkable after handover.

Adjacent engineering

When the problem is not a language model.

Some briefs need a model that is not an LLM, or a sensor stream rather than a prompt. These run through the same evaluation and the same handover.

Machine learning
Forecasting, scoring, and recommendation, evaluated on held-back data before anything ships.
Computer vision
Inspection and extraction from images and video, with a confidence floor that routes the uncertain cases to a person.
IoT and telemetry
Device streams over MQTT with TLS, buffered for loss of connectivity, and aggregated into something a dashboard can read.
AR and spatial
WebXR surfaces where a flat screen genuinely loses information, which is rarer than most briefs assume.

  1. A signal arrives

    Camera frameSensor streamHistoric data

  2. A model scores it

    Scored, not guessed: every output carries a confidence number.

  3. Confidence floor

    The line you set, below which nothing is actioned automatically.

  4. Above the floor, handled

    The system acts and records what it did.

  5. Below it, sent to a person

    No silent guesses on the cases the model is unsure about.

How a build runs

Four phases, each priced before it starts.

  1. Discovery

    We map the workflow, the data, the systems it touches, and where a human has to stay in the loop. The deliverable is a one-page specification and an architecture you can interrogate.

  2. Prototype

    A working slice of the real system, on your data, so decisions after this point are made on evidence rather than slideware.

  3. Production build

    Evals, guardrails, observability, retries, and the boring reliability work that decides whether the pilot survives its first month in production.

  4. Handover

    Source, infrastructure as code, runbooks, and a handover session. Your team owns and operates what we built; we stay on call for your next phase.

Proof

What we have already shipped.

Common questions

Price, timeline, ownership: answered.

What is the difference between an AI agent and an automation?
An automation runs a fixed sequence of steps. An agent chooses the next step within bounds Stallwart writes down first. Agents cost more to engineer because they need an eval suite, a tool allowlist, and a tested override path. Agents earn that cost only when the decision itself is the work.
Do we own the AI system once it is built?
Ownership transfers to you at launch. Source code, prompts, the evaluation suite, Terraform definitions, and the runbooks live in your own GitHub organisation and cloud accounts. Stallwart keeps no licence key and no hosting dependency. Nothing stops working if the engagement ends the week after handover.
How do we know the agent will not go off the rails?
The judgment rule is written before the build. Stallwart defines which tools the agent may call, caps how many steps it may take, and logs every call with its inputs and outputs. Actions touching money or customer identity route to a human for approval. Failed evals block the deploy.
How much does an AI agent or automation build cost?
Pricing is fixed per phase and quoted after a 30-minute scoping call. Discovery, prototype, production build, and handover are each priced and approved separately, so you can stop after any one of them. Running costs stay with you: model tokens, hosting, and any vector database are billed by the provider.
How long does an AI build take?
Timelines depend on how many systems the agent touches. A single-workflow agent on one data source moves faster than one spanning a CRM, a billing system, and a phone line. Across 40+ projects Stallwart averages two to four weeks to launch. The scoping call fixes your phase dates in writing.
LLMs are non-deterministic. How is that safe in production?
Non-determinism is handled with evals. Stallwart pins model versions, sets temperature per task, and runs a fixed suite of real cases on every change. Outputs that must be exact are parsed against a JSON schema and retried on failure. Drift shows up on the dashboard before a customer meets it.
Can you build on the tools we already use?
Stallwart builds against the systems you already run. HubSpot, Salesforce, Zoho, Slack, Microsoft 365, Google Workspace, Xero, QuickBooks, Shopify, and Postgres are common starting points. Where an API exists we use it; where only a webhook exists we build the queue behind it. Replacing your stack is rarely cheaper.

Bring the workflow that eats your week.

One 30-minute call. You leave with an architecture, a fixed price per phase, and a written plan for the first build.

Book a call