Capabilities

The challenges we keep hearing

Reclassify combines an internal lab for proprietary methods and original research with years of client engagements, industry practice, and listening sessions — across enterprises as well as with small and micro-businesses. Different scale, strikingly similar tensions.

Micro-businesses

One or two people doing everything, on the hardware they already own.

Every monthly subscription is scrutinized; free tiers throttle real work.
No IT support, and no appetite for setup or maintenance.
They need tasks completed in the flow of work—not another chat window to babysit.
Client confidentiality matters every bit as much as it does for the big firms.
Whatever laptop they already have has to be enough to run it.

Small businesses

Lean teams who want AI leverage without standing up a data-science department.

No in-house AI talent to select, configure, or maintain models.
Subscription sprawl and metered tokens quietly eat already-thin margins.
Client data needs protecting, but there's no legal team to vet vendors.
AI tools rarely fit the apps, handoffs, and habits that actually run the business.
There's no time to configure anything — it has to work on day one.

Medium businesses

Growing teams balancing client trust, operational complexity, and limited internal capacity.

Client NDAs mean the work simply cannot leak to a third party.
Token meters and usage caps interrupt deep work right when it's flowing.
Teams work across offices, travel, and spotty wifi — and need reliable access to their systems.
AI has to act across the apps they already use, not add busywork.
There is no appetite to manage models, servers, or infrastructure of any kind.

Enterprises

Regulated teams sitting on data they can't hand to a third party.

Sensitive documents and IP can't be sent to public cloud AI — or quietly become someone's training data.
Context is scattered across dozens of tools, silos, and permission boundaries.
Every new AI vendor triggers months of security, legal, and compliance review.
Per-seat, per-token pricing makes real costs impossible to forecast at scale.
The written policy says one thing; the way staff actually work says another — shadow AI is everywhere.

How we work

Research in the lab, systems built around you

Research in the lab

We explore proprietary methods for knowledge representation, contextual reasoning, and transparent AI—turning original research into practical systems.

Bespoke work with clients

We design and implement solutions around each client's data, workflows, constraints, and operating environment. No two systems need to look alike.

Common threads

Four audiences, three shared needs

Once you set scale aside, the same three requests surface in every conversation — and they are exactly what our consulting practice is built to answer.

Data management you own

From regulated enterprises to growing businesses, the real ask is the same: keep ownership of the data. Our consulting starts there — classifying, structuring, and governing your information so it stays an asset your organization can control and use responsibly.

Smart, context-rich systems

Generic tools miss the context that makes an answer useful. We design knowledge-representation systems that understand your business — your terms, your files, your rules — so AI supports decisions with the right material and the right people in the loop.

Transparent, real work

Everyone is tired of black-box chat toys. We build transparent workflows that perform real tasks across the apps you already run, so teams can understand how decisions are made and how each result was reached.

Built from what we heard

These conversations shape our consulting work — data management and smart, transparent, context-rich systems built around your business, drawing on methods refined through work with Fortune 500s, enterprises, and governments. We start with a conversation about the right next step for your organization.