[ENTERPRISE WORK MANAGEMENT] × [APPLIED AI]

Strategy,
instrumented.

Your business evolved past your system. We close the gap, then instrument it so it stays closed.

the gap system capability business demand time that distance is what we close

// WHAT WE DO

Two halves of one loop.

Enterprise Work Management

Your system was built for how the business worked when you built it. We find where the two came apart, and we close the distance inside the system you already run.

  • Demand and Intake Management
  • Portfolio Planning and Scenario Modeling
  • Resource and Capacity Management
  • Financial Management and Adaptive Funding
  • Delivery Execution and Workflow Design
  • Reporting, Analytics and Decision Support
Enterprise work management →
Applied AI

The intelligence layer that sits on top of the work you already run. It’s grounded in how your system was actually designed, and everything it does is measured rather than asserted.

  • Agentic Workflows
  • Integration Layer
  • Private Models
  • Evaluation
Applied AI →

Close the gap.
Keep it closed.

Change doesn’t stop, which means catching up once buys you a year at best. Systems don’t fall behind because someone built them badly. They fall behind because the business kept moving and the system didn’t. That’s why the loop is the product, not the project.

ENTERPRISE WORK MANAGEMENT

Work,
engineered.

The work still gets delivered. It just stops running through the system you built for it, and the record stops matching reality.

shadow work the system intake delivery

// WHERE YOU’RE STARTING

You already run a system

It was built for the business you were at the time. Since then you’ve reorganized, taken on new programs, and changed how work moves through the company, and the system has stayed where it was. We measure how far apart the two have drifted, then close the distance.

Start with a Signal Read →

You’re standing one up

A new build is the one moment you get to design for how the business will run, rather than only for how it runs this quarter. We scope for change from the beginning, so the system isn’t already behind by the time everyone’s using it.

Work with us →

// HOW IT RUNS

Map

Find where the gap opened. We read the system and we talk to the people living in it, across every team the work touches, including the ones who quietly stopped using it. What flows, what stalls, and where the business has moved past what was built for it.

Engineer

Close it. We rebuild the process into the system, in your environment and alongside your team. You don’t get a recommendation deck at the end of it. You get a system that runs differently on Monday.

Map and Engineer are the first half of the loop. Applied AI runs the second half, Drive and Tune, so the gap doesn’t reopen.

The Signal Read

Your business moved.
Did your system?

Your system holds years of history, and it's the most honest record you have of how the work actually runs. It's also the foundation any useful AI has to sit on. The Signal Read turns that history into a measured baseline and a ranked plan for what to build first.

// HOW IT RUNS

Phase one

Read the system

We pull the adoption and integrity signals from your live data, and talk to people across every team the work touches, including the ones who stopped using it.

Phase two

Measure the distance

We measure how work runs today against how the system was designed to run it, and separate where AI has leverage from where the process needs to catch up first.

Phase three

Rank and plan

We present findings to your leadership team, ranked by effort and impact, with a plan for what to build first.

// WHAT YOU GET

REPORT

Signal Report

The state of your system in plain language: where work flows, where it stalls, and where the business has moved past what the system was built for.

BASELINE

Baseline Scorecard

Today's numbers, captured: active usage, field completeness, stale records, shadow work. Performance metrics wherever your data supports them.

MAP

Opportunity Map

Every opportunity sized by effort and impact, so the sequence is obvious and the quick wins are visible on day one.

BUILD PLAN

Build Plan

The specific workflows we should stand up next, each with an expected outcome and what it takes to build.

// WHAT WE DO

Demand and Intake Management

Portfolio Planning and Scenario Modeling

Resource and Capacity Management

Financial Management and Adaptive Funding

Delivery Execution and Workflow Design

Reporting, Analytics and Decision Support

// HOW WE ENGAGE

  • Implementation and Migration
  • Configuration and Optimization
  • Adoption and Enablement

Where did your gap open?

Work with us

Applied AI

Intelligence,
grounded.

The intelligence layer that sits on top of the work you already run. It’s grounded in how your system was actually designed, and we’re accountable for whether it works.

INTELLIGENCE LAYER workflows models evals integrations WORK BACKBONE

The difference

Your system holds your data.
It doesn’t hold your intent.

Your platform's built-in AI knows what's in your data. What it can't know on its own is why the system was configured the way it was: the process logic, the trade-offs, the business intent behind every decision.

That context isn't in the data anywhere. Somebody has to reconstruct it. The platform gives you the foundation, but the last mile is always local.

Grounded

In your configuration

We reconstruct the intent behind the setup: the process logic and the trade-offs that were never written down.

Connected

Across your stack

Work doesn't stop at the platform boundary, so neither do we.

Measured

Not asserted

Every system we run ships with quality checks that show whether it's accurate enough to trust.

// THE OPERATING MODEL

The risk isn’t the technology.

The biggest risk in any technology change is using new capability to preserve an old operating model. An agent gets pointed at a process that was designed around people doing every step by hand, and what you end up with is a faster version of something that already wasn’t working.

Moving a rung means redesigning who does what, not buying a tool. Most enterprises are somewhere between the first two rungs. The ambition usually sits further right than the operating model does.

Speed and control aren’t opposites. Trusted data, clear decision rights, and human oversight are what make speed safe. Governance should be infrastructure for speed, not a brake.

// WHAT WE BUILD

Agents that know what
the work means.

A generic agent is capability without context. It can read what’s in the record, but it has no way of knowing why the record looks the way it does. We build on top of a configuration we’ve mapped first, so the agent knows the process logic, the trade-offs, and the intent behind the fields it’s reading.

Agentic workflows

Work that moves itself

Intake triage, routing, and status generation running against your live work, with human review where the decision matters.

Integration layer

Beyond the platform boundary

MCP servers and APIs connecting the work backbone to the systems around it, so your data reflects reality without manual updating.

Private models

Your data stays yours

Self-hosted and private LLM deployments for organizations where the data can’t leave the building.

Evaluation

Measured, not asserted

Every agent ships with evals: quality checks that show whether it’s accurate enough to trust before it runs unsupervised.

// WHY IT COMPOUNDS

Every gap you close should
make the next one cheaper.

Most transformation work is disposable. The consultants leave, the knowledge leaves with them, and the next change starts from zero.

Instrumentation is what changes that. When the process logic is captured, the data is trusted, and the agents running the work already understand what it means, the next change starts from a system that knows itself. The organization doesn’t just get adapted. It gets faster at adapting.

The organizations that learn fastest will outperform the organizations with the best AI.

Catching up once isn't the point.

Your business will keep moving, so a one-time fix starts aging the day it ships. The Signal Read ends with a build plan and a clear first move. The natural next step is a Drive engagement: standing up the top opportunities, running them live, and tuning them each quarter so the gap doesn't reopen.

Applied AI starts where work management leaves off. If you haven't mapped the gap yet, start with a Signal Read.

Work with us

References

Ask the people
who've worked
with us.

Recommendations from the people who ran the work alongside us. Reference calls available on request.

Enterprise work, at enterprise scale.

Real estate & facilities

Global portfolios: design and construction, occupancy planning, moves and exits.

Construction & capital projects

Capital planning and program delivery across distributed project teams.

Technology & product

Product portfolios, roadmap governance, and delivery at scale.

Manufacturing & industrial

Plant, supply chain, and operational programs across regions.

Bridge the gap between business strategy and technology execution.
Former client · now Professional Services, Planview

Worked with Hyperleap for nearly three years across multiple large-scale systems implementations.

Read the full recommendation
Grasps complex client needs — even the ones clients themselves struggle to put into words.
Director, PMO Lead

Partnered on a global project management platform build for a design and construction organization.

Read the full recommendation
One of the most well-rounded consultants and technology leaders I've worked with.
Multi-year, cross-organization

Worked alongside Hyperleap across two organizations over seven years, from a capital planning implementation to a global enterprise work management platform.

Read the full recommendation

About

Every seat
at the table.

Inside the vendor. Running our own practice. Sitting on the client side. We’ve built this from all three.

Forty-plus years across the team. Eleven of them on one platform, unbroken.

2015 2019 today inside the vendor our own practice

We started inside the vendor in 2015, at Clarizen, now Planview AdaptiveWork. First in Professional Services delivering implementations, then in Sales Engineering, where the job was working out what a business actually needed before anyone wrote a line of configuration. Since 2019 we've been on our own side of the table, building for enterprises in construction, product, finance, and IT.

That combination is unusual. Most consultants learn a platform from the outside. We learned it from inside, across both delivery and pre-sales, then spent eight years applying it.

Eleven years inside enterprise work management isn't narrowness. It's the reason we can walk into a system and see, in an afternoon, where the business has moved past what was built for it.

Hyperleap is an authorized Planview partner, for both implementation and licensing. We say that up front because you should know where we stand before you weigh a recommendation we make.

The model

Gaps don't close from the outside.

We work the way a forward deployed engineer works. Embedded with your team, hands-on in the system itself, and there through the whole lifecycle. We’re not here to hand off a plan and leave. We’re here to be accountable for whether the work actually runs better afterwards.

That means we're in your system, not in a conference room. We stay past the recommendation. And when something we built doesn't work the way it should, we're the ones who fix it.

Hyperleap works as a senior team: specialists brought in per engagement rather than a bench on payroll. Collectively, more than forty years on this platform.

Trusted advisors who can guide an engagement end to end. Recommendation on LinkedIn · read it

// THE TEAM

Who actually does the work.

Specialists brought in per engagement. Everyone here has spent years on this platform.

Ethan Treesara

Principal

Eleven years on the platform, three of them inside the vendor across Professional Services and Sales Engineering. Leads engagements end to end, from mapping the gap to standing up what closes it. Before that, consulting at Protiviti and product work in healthcare and education technology.

Jeff White

Chief Architect

A decade on the platform across Clarizen and Planview, plus enterprise architecture for a national bank’s program management office. Designs the agentic workflows, integrations, and evaluation layers that Applied AI engagements run on, and has shipped them in production.

Kyle Schworer

Lead Engineer

Came to the platform from the consulting side, delivering enterprise implementations at Deloitte and IBM. Deep in capital planning and large-scale program delivery for global manufacturers.

Charles Hilliker

Lead Engineer

Six years inside the vendor as a senior enterprise consultant, where he led more than a hundred enterprise implementations. Specializes in configuration, workflow design, and getting complex rollouts to land on time.

Shane Ly

Solutions Architect

Six years inside the vendor in Professional Services, now a solutions architect in enterprise SaaS. Focused on integration design and connecting the work backbone to the systems around it.

What we believe

Three things we've stopped arguing about.

Everything is managed work.

Construction programs, product roadmaps, capital planning, IT delivery: different vocabularies, same underlying shape. Portfolios of work competing for the same finite capacity.

Change is the constant.

Systems don't fail because they were built wrong. They fall behind because the business kept moving. That's why the loop matters more than the launch.

AI is only as good as the process underneath it.

Pointing AI at a broken workflow produces faster mistakes. Fix the process, instrument it, then let AI run it.