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.

Book a consultation How our quarterly model works

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.

See how we work

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.

Explore the quarterly delivery cycle

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.

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

Explore SHIP methodology

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

Cycle-time reduction

Pending real data

X→Y

Decision speed, days to hours

Pending real data

Target-user adoption after Y weeks

Pending real data

See the work

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 case	Result
Extract invoice total	Pass
Route support ticket	Pass
Summarise long contract	Review
Reconcile ledger entries	Pass

Agent monitoring

X ms

P95 latency

Success rate

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

Explore the delivery system

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.

Book a consultation Explore the quarterly delivery cycle