Custom agents for the jobs only your business has
Research agents, reporting agents, knowledge brains. Scoped to one workflow, wired into where the work already happens.
Your custom strategy and systems, delivered in under a month. Your named people using them every week by day 30. Or we keep working for free until both are true.
Drawn from this system's own stages. A dashed ring marks a stage where a person acts.
01How it runs
Stage by stage.
What we won't build: agents that make final calls on legal positions, investment decisions, or anything your regulator expects a human to sign. Agents assemble, retrieve, draft and monitor. People decide.
- 01
Scoping
One workflow, chosen because senior hours meet assembly work there. Not a platform. A job.
- 02
DOE architecture
Directives your team can read and edit in plain text. Orchestration that decides what runs when. Execution tools that do the work. You can see all three layers.
- 03
Integration
The agent lives inside the tools your team already uses. No new destination, no new login, no adoption cliff.
- 04
Evaluation
Tested against real cases with your people judging output before anything deploys.
A person acts here
- 05
Training
Your team learns to run it, tune it and extend it. The directives are theirs.
02What it does
Four jobs it takes on.
Research analysts
Agents that read your documents and return cited answers instead of guesses.
Executive assistants
The collecting, formatting and re-keying handled before your best people sit down.
Packaged teammates
Proven agent builds adapted to your stack, live in weeks rather than quarters.
Objective loops
An agent that works toward a stated goal, checks its own output and reports progress.
What it replaces
You evaluated six tools. None fit.
Your workflow is specific to you. Off-the-shelf software keeps asking you to change it.
Your knowledge lives in inboxes.
Every answer the firm has ever produced exists somewhere. Finding it takes longer than redoing it.
The work is judgment plus assembly, and assembly wins.
Your best people spend their hours collecting, formatting and re-keying before they get to think.
Vendor AI is a black box.
You can't see why it answered, can't change how it behaves, can't take it with you.
Pilots demo well and die quietly.
The proof of concept worked. Nobody wired it into the real workflow, so nobody used it.
03Proof
Where it runs.
Hoyes Michalos
LiveCustom agents built for the firm's own workflows, with a person approving anything that writes.
Read the story630automated runs in the 30 days to 15 October 2026System loggedby Advizr, 15 Oct 2026How this was measured
Scheduled jobs, curator passes and the corpus refresh, with Advizr staff and test accounts left out.
Picton Mahoney
LiveEverything the engine finds waits in a review queue for a person to approve, reject or send back.
Read the story111commits on an engine repository that forks nothingBuild artefactby Advizr, Aug 2026Hungerford
DeliveredScouts that read permits and listings, with outreach drafts a person edits and sends.
Read the story1,808listings the scout read and keptSystem loggedby Advizr, 20 Aug 2026
04What is included
In the build.
- One scoped agent, built on DOE, running on your isolated infrastructure
- An evaluation harness with your real cases
- Plain-text directives your team owns and edits
- Training, drift monitoring and iteration, inside the monthly partnership
05How it is built
Five layers under every system.
The same five layers sit under all 14 systems. Here is each one in this system's own terms.
Retrieval
Agents work from your documents and data rather than general knowledge, and every answer carries its source. Retrieval quality is measured separately from generation, and before it.
- pgvector
- Full-text BM25
- Reciprocal rank fusion
Agents and orchestration
Each agent has a defined skill set, an autonomy level, a spend ceiling and a promotion gate it must clear before acting without review. Tool access goes through one protocol rather than one-off integrations.
- agent-worker
- Autonomy guard
- Promotion gate
Evaluation
An agent that has not been evaluated is a demo. Graded sets run before a change ships and again after, and the harness resolves which evals a change actually touches.
- Eval graders
- Regression targeting
- quality-worker
Models
Model per job, not one model everywhere. We default to frontier reasoning for tool use and long documents, and reach for something smaller or a plain classifier when it measures better on the specific task.
- Frontier models through one gateway
- Embedding models via the same gateway
- Task-specific classifiers
Data boundary
Your data lives in your own database, one part of the isolated infrastructure each client gets. Access follows each person's role, actions are recorded, and an agent works inside the level a person set for it.
- Supabase row-level security
- Scoped agent memory
- Training off without your consent
Named tools in this buildFrontier models, MCP, Agent runtime, Supabase
06How we build it
In this order.
- 01
One workflow, binary success criteria.
The agents that survive contact with production are scoped to a single workflow where success is unambiguous. Broad assistants are the ones that quietly get abandoned.
- 02
Gate on evals before autonomy.
An agent earns autonomy by clearing a graded set, not by seeming impressive in a demo. Published pilot-to-production rates are the reason this gate exists.
- 03
Widen scope only from measured ground.
New skills are added one at a time, each with its own evals. Scope creep without measurement is how a working agent becomes an unreliable one.
What we will not automate
Building an agent where a script would do, and shipping anything customer-facing without a human gate and an eval suite.
07Questions
Straight answers.
What's the difference between an agent and an automation?
An automation follows a fixed path. An agent runs a judgment loop: assess, decide what is needed, fetch it, draft, check. You use automations where the path never changes and agents where it does.
How do you stop it from making things up?
Grounding in your data, citations on claims, human checkpoints at the decisions that matter, and an evaluation harness run before deployment. If a job cannot tolerate any error, the agent assembles and a person signs.
Who owns what we build?
Your directives and your data are yours. The directives are plain text your team edits, and your data exports whenever you ask. The Advizr Platform is licensed to you, and its source code stays with us.
Which model runs my agents?
Each agent runs on a model chosen for its job, from the 32 text models the Platform offers across 9 labs. Your admins choose which models may run, and each tier has fixed backups. We show you the comparison rather than selling a preference.
What does maintenance look like?
The post-deployment phase: drift monitoring, fixes and quarterly iteration, all inside the monthly partnership. Your data and your knowledge base stay yours.
What won't you build?
Agents that make final calls a regulator expects a human to sign: legal positions, investment decisions, trust money movements. Assembly yes, judgment no.
Built alongside it
One inbox
Every message lands in one inbox and every reply waits for your OK.
Voice agent
Every call answered in your voice and hours, and every call filed after.
Run your whole company on AI.
Tell us how your business runs. If we're not the right fit, we'll say so on the call.
Your custom strategy and systems, delivered in under a month. Your named people using them every week by day 30. Or we keep working for free until both are true.
The AI operating layer for enterprises.