[ENTERPRISE WORK MANAGEMENT] × [APPLIED AI]

Strategy,
instrumented.

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

Three years inside a platform vendor, eight running our own practice. Embedded with your team, hands-on in your actual system, accountable through the whole lifecycle.

Applied AI → See references

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

Enterprise work management
Consulting · strategy

Enterprise work management. We find where the business moved past the system, then close that distance in your actual instance.

  • Portfolio and work management
  • Delivery design and enablement
  • Data, reporting and feedback loops
See the work →
Applied AI

The accountable intelligence layer on top of the work you already run — grounded in how HyperLeap built your configuration, and measured rather than asserted.

  • Grounded in your configuration
  • Accountable for the outcome
  • Measured, not asserted
Applied AI →

Close the gap.
Keep it closed.

Change is continuous, so catching up once is temporary by definition. A system falls behind not because it was built wrong, but because the business kept moving.

That makes the loop the product rather than the project.

Where did your gap open?

Start a conversation

Applied AI

Strategy,
in motion.

The intelligence layer on top of the work you already run — grounded in how HyperLeap built it, accountable for the outcome.

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INTELLIGENCE LAYER WORK BACKBONE

The difference

A tool feature can't be
accountable. A partner can.

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

We do, because we authored it. The platform brings the foundation. The last mile is local.

Grounded

In your configuration

We know why every switch is set the way it is, because we set it.

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.

// 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; it can’t know why the record looks that way. We build agents on top of a configuration we authored — grounded in the process logic, the trade-offs, and the intent behind every field.

Agentic workflows

Work that moves itself

Intake triage, routing, and status generation running against your live portfolio — 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 the portfolio 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.

In production today: risk and action extraction from project conversations, workplan quality scoring, stakeholder communication drafting, and portfolio sentiment analysis.

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

The Signal Read

Your business moved.
Did your system?

Your AdaptiveWork instance holds years of history. It's the most honest record you have of how work actually runs — and the foundation AI needs to be useful. The Signal Read turns that history into a measured baseline and a ranked plan for what AI should run next.

01
Week one

Read the instance

We pull the adoption and integrity signals from your live data, and interview four to six stakeholders — including at least one skeptic.

02
Week two

Measure the distance

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

03
Week three

Rank and plan

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

01 · REPORT

Signal Report

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

02 · BASELINE

Baseline Scorecard

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

03 · MAP

Opportunity Map

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

04 · PLAN

Nova Build Plan

The specific workflows we should drive and tune next — each with an expected outcome and a cost to stand it up.

Fixed boundaries, so nothing
surprises either of us.

Included
  • Adoption and integrity baseline from your live instance
  • Performance metrics wherever your data supports them
  • Four to six stakeholder interviews
  • One agreed portfolio or business unit in scope
  • All four artifacts, yours to keep
  • One live readout to your leadership team
Not included
  • Building, configuring, or changing anything in your instance
  • Implementing any recommendation from the report
  • Additional portfolios beyond the agreed scope
  • Data cleanup or remediation
  • Ongoing advisory after the readout
Each of these is real work — it belongs in a Drive engagement, scoped and priced on its own.

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 number. 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.

Start a conversation

References

Ask the people
who've worked
with us.

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

Want a reference call?

Several past clients have offered to speak with serious prospects directly. Ask and we'll arrange it.

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About

Eleven years.
Both sides
of the table.

Three years inside the vendor. Eight running our own practice. Same discipline the whole way through.

2015 2019 today inside the vendor our own practice eleven years in

We started at Clarizen in 2015 — first in Professional Services delivering implementations, then Sales Engineering, where the job was understanding 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 an instance 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 your actual system, staying through the whole lifecycle. Not here to hand off a plan — here to be accountable for whether the work actually runs better.

That means we're in your instance, 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 small senior team — specialists brought in per engagement, all with production experience in the platform.

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

Where did your gap open?

Start a conversation