AI and automation
Anthropic
Technology in service of the outcome.
Integrate Claude into knowledge, coding and business workflows where evaluation shows a good fit, with explicit tool permissions and accountable human review.
- Prompt
- Written policy
- Claude
- Human review
- Released answer
- Audit log
Prompt passing a written policy check before a Claude model, then human review of the result.
Claude can help people work through dense documents, prepare structured material and coordinate bounded operational tasks. Emerge focuses the implementation on the work surrounding the model: intake quality, trustworthy context, reviewer control and a traceable connection between the original source and the final output. A useful document workflow should make checking easier, not merely produce a longer summary.
Our approach covers Claude through an appropriate supported deployment channel, including Amazon Bedrock where the AWS architecture fits. We assess model suitability using representative material and keep model choice separate from tool authority. The resulting application can preserve the same review process and business integrations when the model configuration changes.
Choose the right starting point
Product expertise, in detail.
Document workflows
Document intake, contextual extraction, comparison and review experiences that retain a link to the source material.
Enterprise agents
Claude connected to approved tools with explicit permissions, bounded task state and human control of consequential actions.
Bedrock delivery
Assess Claude within AWS identity, knowledge and operating services when the organisation’s cloud requirements make that channel appropriate.
The starting point
What needs to change.
Long documents contain tables, definitions, exceptions and references that lose meaning when flattened into undifferentiated text. The ingestion design must preserve enough structure for extraction, comparison and human verification.
Operational assistants need to distinguish a proposed plan from an authorised business action. Access to a capable model does not establish permission to send a message, amend a record or publish a document.
A source may be incomplete, contradictory or outdated. Reviewers need visible uncertainty, linked passages and a queue for exceptions rather than an apparently definitive output that conceals missing evidence.
What we deliver
From opportunity to working systems.
Structured document preparation
We define the fields and sections a downstream team needs, then combine extraction with terminology and source context. Review screens show the original passage beside the proposed result. Corrections are captured as reviewer decisions and feed a controlled improvement process.
Evidence-led knowledge assistance
A user question is connected to the approved collection and the user’s permissions. The application retrieves relevant material, distinguishes quotations from interpretation and provides an escalation path when evidence does not resolve the question.
Assisted operational work
The agent can gather context, prepare a task plan and request permitted tools. The application validates each tool request and records its outcome. Tasks that exceed scope or require approval remain visible to the responsible person.
Illustrative solution architecture
How the pieces work together.
Bring context into the workflow, connect the right solutions, and make progress visible.
01 Understand the context
Signals & knowledge
Use the context and information already in place.
- Business priorities
- Trusted knowledge
- Operational data
02 Connect the solutions
Structured document preparation
Evidence-led knowledge assistance
Assisted operational work
03 Put it to work
Teams & operations
Connect people and systems to the next useful action.
Measured outcomes
Track agreed measures, learn, and improve the workflow.
Built around your existing technology
Implementation design
How the pieces work together.
Source, interpretation and approval
The workflow stores an original document, extracted structure, a model-produced interpretation and the reviewed output as separate artefacts. This makes it possible to identify whether an error began at ingestion, model interpretation or manual approval and to correct the right stage.
Tool execution behind application controls
Claude tool use supplies structured requests to application-defined tools. Emerge keeps authentication, permission checks and execution in the application boundary. A read-only lookup and a write operation have different scopes, logging and confirmation requirements.
Decisions to make early
Context length does not replace evidence design
Sending more pages can increase cost and still leave important relationships unclear. We select extraction, retrieval or direct document input according to the task, then test whether the answer preserves the meaning of tables, references and exceptions.
Deployment is a separate decision
Model availability, supported features, regional options and data handling depend on the selected channel and account. These are confirmed for the proposed implementation. The page describes Emerge’s service scope without asserting an Anthropic partner tier or inherited certification.
Our approach
A clear path into delivery.
Start with the business problem. Make each stage useful, reviewable and owned.
- 01
We sample the document families or task types involved and define the reviewer’s rubric: missing fields, unsupported statements, incorrect terminology and failed actions are separate error classes. The sample includes difficult scans and contradictory sources where relevant.
- 02
The first release candidate delivers one complete workflow from intake to reviewed result. It includes an exception queue, retained source identifiers, visible task status and a recovery path if extraction or model processing fails.
- 03
Handover supplies the evaluation collection, tool inventory, approved configuration, reviewer guidance and operational runbook. Product changes are assessed against the same examples before rollout, with usage and review effort measured together.
Go deeper
Build a more informed brief.
Related Agentforce integration guides for Anthropic.
These integration guides examine specific systems, access boundaries and illustrative use cases.
Practical questions
Before we begin.
Can Claude approve a sensitive document automatically?
We design the approval responsibility around the organisation’s process. The model may prepare a draft or flag an exception, while a qualified reviewer remains responsible for consequential approval. Automated low-risk steps need explicit criteria and observed evidence.
What happens if a document cannot be interpreted reliably?
The workflow preserves the original, marks the unresolved fields and routes the item to review. It avoids silently filling gaps with plausible values. Operators can correct an extraction, add approved context or complete the task manually.
Explore the detail
Related work and resources.
See the approach in context. Client engagement stories are anonymised; related examples may come from other sectors or platforms.
Connected expertise
Technologies
Platforms
Solutions
Your next move
Bring us the business problem.
Pick a time below. We will help you define a useful starting point, the expertise you need and a practical path to delivery.
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