05 / KNOWLEDGE / ACTIONINTERNATIONAL DELIVERY

AI agents for business tasks that need context and human control.

AI agents for business can retrieve information, prepare answers and assist with defined tasks inside a team’s tools. We scope the sources, permissions and review points before implementation. The service is for teams that need context-aware support while keeping people in control of important decisions and actions.

Starting from $8,000Typical range: $8,000 to $40,000 · 4 to 12 weeksSee full pricing Starter projects from $200

Book a free AI agent call See how it works

Free discovery callNDA available

RESEARCH ASSISTANT / CONCEPT VIEW SIMULATED

QUESTIONSummarise the latest supplier review.

SOURCES RETRIEVED

Supplier review — Q3.pdf92% relevant

Operations notes.docx84% relevant

ANSWER WITH SOURCES

The review shows three recurring issues: delivery variance, unclear ownership and incomplete status updates.

[1] Q3 review[2] Ops notes

HUMAN REVIEWDraft sent

01Where teams lose time to context

Good decisions need the right evidence.

Teams lose time searching through documents, inboxes, chat threads and spreadsheets. The same questions return, and useful knowledge stays with one person.

DocumentsInboxOne expertSpreadsheets

Time lost searching

People spend hours finding the context needed to start useful work.

The same questions repeat

Answers are rewritten instead of being retrieved and checked.

Knowledge stays in one head

Important decisions depend on whoever happens to remember.

02Intelligence that works inside your context

Build a focused agent, not a general chatbot.

Connect the sources, rules and tools that matter to one useful task. Return a sourced answer and keep people in control of important actions.

InboxPDFsChatSheets

Sources→Focused agent→Sourced answer

One focused agent inside your context.

03Capabilities

Put useful intelligence next to the work.

Research agents

Turn a question into a structured research task.

Example: gather sources and prepare a brief.

Knowledge assistants

Help teams find answers in their own context.

Example: answer policy questions with citations.

Document processing

Extract useful structure from long documents.

Example: compare clauses or classify files.

Automated research

Repeat a research pattern without losing the sources.

Example: monitor updates and prepare a digest.

Internal tools and dashboards

Put useful intelligence next to the work.

Example: a review queue with source and status views.

Agent actions and integrations

Let agents prepare actions across your stack.

Example: draft an email for human approval.

04Agent anatomy

Every step should be checkable.

Tap a layer to see what we build. Human review is a deliberate control point, not an afterthought.

FOCUSED
AGENT

SELECTED LAYER

Retrieve

Find relevant, permitted sources.

Human approval is a deliberate control point.

05Illustrative impact

Spend less time finding and summarizing information.

The figures below are illustrative, not a client result. We measure the real workflow before recommending an agent.

Manual searching28h

Agent-assisted12h

50/50

Illustrative example, not a client result.
Searching versus decision-making.

06Interactive workflow

AI Research Assistant — concept demo.

AI RESEARCH ASSISTANT / CONCEPT DEMO

Keep people in control.

Concept simulation. Not a client outcome.

Human approval before action

01Question

02Agent

03Knowledge

04Tools

05Action

06Result

SAMPLE ANSWER

Three issues found in the supplier review. [1] [2]

Sources: Q3 review · Ops notes

Ready to simulate0%

LIVE LOG{"step":"waiting","review":true}

07Trust and control

Agents you can check and control.

Your data

Access rules

Agent

Human review

Output

LOG TRAILsource_checked → permission_validated → draft_created → approval_required

Source citations, permissions, audit logs and approval steps make the system easier to trust.

08Example use cases

Focused jobs for real teams.

Sales research briefExample use case

Support answer draftingExample use case

Contract or document reviewExample use case

Internal knowledge searchExample use case

09Our process

Six steps from context to useful action.

Timings below are illustrative; actual scope and dependencies determine the project schedule.

Understand

Clarify the task, sources and boundaries.

1 weekUse-case brief

Architect

Map data, access, tools and review points.

2–3 daysAgent map

Design

Shape the prompts, outputs and human flow.

1 weekInteraction design

Build

Develop the agent in useful increments.

2–4 weeksWorking agent

Integrate

Connect sources, tools and permissions.

1 weekConnected system

Improve

Review outputs and refine the next version.

OngoingImprovement plan

10Integrations map

Connect agents to the tools and data you already use.

AGXAGENT

DocumentsKnowledge baseCRMEmailSlackDatabasesREST APIs

We connect agents to the tools and data you already use.

11Technology

Choices for accuracy, control and handover.

OpenAI APIs

Useful model access with clear provider boundaries.

Anthropic APIs

Another option for capable, controlled workflows.

Knowledge bases

Retrieve relevant sources instead of guessing.

REST APIs

Connect tools at the level the task requires.

Node.js

Clear application and integration logic.

PostgreSQL

Structured data and understandable records.

12What you get

Useful output,
not just activity.

01Use case and data map

02Working agent or internal tool

03Connected sources and tools

04Review and approval flow

05A practical improvement plan

13Engagement options

Choose the right starting point.

AI Agent Audit

For teams that need a grounded starting point.

Use-case review, data map, risks and next-step plan.

Typically 1–2 weeksGet a quote

Agent Build

For teams ready to put one focused agent to work.

Agent, sources, tools, review flow and handover.

Typically 3–6 weeksGet a quote

Ongoing Agent Support

For teams improving agents over time.

A practical partner for outputs, integrations and controls.

Scoped togetherGet a quote

14Security and trust

Useful intelligence with control.

NDA available

Your data stays in your control

Role-based access

Source citations and audit logs

You own your agents and workflows

15FAQ

Questions worth answering early.

01What tasks are suitable for an AI agent?

A suitable task has a clear purpose, permitted information sources and an output a person can evaluate. High-impact or ambiguous actions need explicit human oversight.

02Can an agent use our documents and internal data?

It may be possible to connect approved sources, subject to access controls, data boundaries and the technical platform. The permitted sources and users must be specified before build.

03How do you reduce unsupported AI answers?

A design can constrain the task, retrieve approved sources, show citations and route uncertain cases for review. These controls reduce risk but cannot guarantee error-free output.

04Will our data be used to train a model?

That depends on the selected provider, configuration and agreement. Data use, retention and provider settings should be checked and documented before any sensitive data is connected.

05Can an agent take actions without approval?

Action permissions are scoped to the use case. Important, sensitive or hard-to-reverse actions can be limited to drafts or require a human approval step.

06How do you test an agent before release?

Testing should use agreed examples, edge cases, source checks and access scenarios. Acceptance criteria and monitoring are set for the task; test results do not guarantee perfect future answers.

16Next step

Let's put intelligence to work.

Tell us what task should be easier. Use the project form to describe the current workflow and the result you need.

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