Turn your high-value workflows into production-deployed AI systems.

Prople is a GenAI implementation partner to businesses on their AI journey. We help our clients identify high-value workflows, build working Agentic AI systems, and deliver results in production.

Start with our fixed-fee model and ship two prioritised use cases into production by the end of the quarter.

Problem statement
Scoped use case
Deployed AI system
Validated outcome

AI ambition is high. Implementation capacity is the constraint.

The bottleneck is rarely a shortage of ideas. In practice, it looks like:

  • Disconnected experiments that never add up to anything
  • Pilots that stall before they reach production
  • No reliable way to decide which workflows to tackle first
  • Not enough hands with real implementation experience
  • Business and technology teams working on separate tracks
  • Success that no one can actually measure

Prople moves AI out of the experiment stage and into daily operations.

Start with fixing the broken process. Not the technology.

Find

Identify the workflow

Identify workflows where the value, feasibility, and adoption conditions justify implementation.

Build

Build inside the business

Embed with business and technology teams to create the working system.

Prove

Prove the result

Measure performance, adoption, and operational results before deciding what scales next.

One quarterly delivery cycle. Two use cases.

Weeks 1–2

Opportunity assessment

Diagnose operating problems and prioritise two candidate use cases.

Weeks 3–12

Build and implementation

Architecture, implementation, integration, evaluation, and adoption.

End of cycle

Results and next step

Results, handover, and a recommendation: repeat the cycle or scale.

There is a concrete way to start.

SHIP — Prople's implementation methodology

A disciplined path from problem to proof.

Every implementation runs through SHIP: Scope, Harness, Implement, Prove — an iterative loop where each cycle feeds the next.

an iterative loop each cycle feeds the next S Scope H Harness I Implement P Prove
ship-gate-review
Scope
Inputs
Candidate workflows, stakeholder input
Gate
Value, feasibility & risk threshold
Outputs
Prioritisation scorecard, success measures
Gate passed → Harness
Harness
Inputs
Prioritisation scorecard
Gate
Architecture & evaluation plan signed off
Outputs
Architecture template, evaluation design
Gate passed → Implement
Implement
Inputs
Architecture & evaluation plan
Gate
Test & deployment gates passed
Outputs
Deployed system, observability
Gate passed → Prove
Prove
Inputs
Deployed system, monitoring data
Gate
Business outcome & adoption measured
Outputs
Outcome report, next-cycle decision
Feeds next Scope

Start where the problem is.

Implement

For organisations ready to put meaningful use cases into production.

Scale

For organisations ready to build repeatable implementation capability.

Lead

For organisations needing senior AI leadership, architecture, and delivery expertise.

Build products

For innovators and investors turning repeated industry problems into AI products.

Need alignment first? Start with an executive briefing or opportunity workshop designed to create an implementation decision.

Proof is part of implementation.

X hrs
Operating capacity returned per week
Pending real data
X%
Cycle-time reduction
Pending real data
X→Y
Decision speed, days to hours
Pending real data
X%
Target-user adoption after Y weeks
Pending real data

Built to move quickly. Designed to hold up in production.

ship-toolchain
Agent templates
Support triage
Document extraction
Workflow router
Data reconciliation
Setup & harness scripts
1def load_source(config):
2  df = extract(config.uri)
3  df = validate_schema(df)
4  return transform(df)
5
6def register_tools(agent):
7  agent.bind(retrieval, actions)
Evaluation framework
Test caseResult
Extract invoice totalPass
Route support ticketPass
Summarise long contractReview
Reconcile ledger entriesPass
Agent monitoring
X ms
P95 latency
X%
Success rate
X
Runs today

We accelerate implementation by reusing what should be reusable and customising what must be specific to your environment: agent templates, setup and harness scripts, evaluation frameworks, and the monitoring tooling to run agents in production.

Integrated toolchain Reusable components Architecture patterns Governance controls Deployment patterns

The same people make the call and do the build.

Commercial operators

Understand value, operating constraints, and executive decisions.

AI builders

Design and implement the actual system.

Embedded delivery

Work with the people who own, operate, and adopt the workflow.

What operating problem would you solve first?

Bring us a workflow that costs too much, takes too long, or breaks too often. We will help determine whether it is worth implementing with AI.