Solutions Architecture Work Why KeenDigit Insights Discuss your AI project
Enterprise AI-native application development

Take AI-native ideas
into controlled production.

We build AI-native applications and workflows around real enterprise systems — grounded in your data, evaluated before release, guard-railed, and human-reviewed. Every pipeline ships with its evidence attached.
Discuss your AI project See what each stakeholder needs Glass box, not black box.
Workflow · Contract review assistant
RUNNING
Input
request + docs
Grounding
12 sources
Model
reasoning
Guardrails
PASS
Human
2 FLAGGED
Output
DELIVERED
Groundedness
0.96 / 1.00
Eval suite
48 / 50 passed
Guardrails
PASS
Latency p95
840 ms
Continuous delivery v1.3 → v1.4 → v1.5 live · shipped 3× this week, zero regressions
Already in production — generating revenue across these industries
Finance
Healthcare
Retail
Logistics
Manufacturing
Server → LLM
Whole stack, one owner
Every release
Ships with its evidence attached
100% independent
Vendor-neutral · no lock-in
Hands-on
Senior engineers, every project
One project · three decision lenses

Enterprise AI moves when
the whole committee can say yes.

Business, technology, and risk leaders are approving the same system from different angles. We give each of them the evidence they need without splitting the project into three competing tracks.

Business & innovation

Turn a valuable workflow into an operating AI capability

"We can see the opportunity. We need a credible path from use case to business operation."

We frame the workflow, operating boundaries, and release path together so the project is tied to a real business decision — not a technology demo looking for a home.

A use case with a defined operating outcome
Clear human and system responsibilities
A phased path from pilot to production
Discuss the business workflow
Product & technology

Ship AI features that hold up beyond the demo

"We tried an LLM demo. It impressed everyone, then fell over in production."

We connect models to the data, APIs, applications, and infrastructure they depend on — then gate releases with evaluation, guardrails, observability, and rollback paths.

Features that pass eval gates before users see them
Continuous delivery, with instant rollback
Evidence on every release, not just a demo
Discuss the technical path
Risk & governance

Keep control visible as AI enters the workflow

"We need to know what the system can do, who approves changes, and when a person steps in."

Our glass-box discipline keeps access boundaries, review checkpoints, release decisions, and change records visible from design through operation.

Documented approvals and access boundaries
Human-review rules for high-stakes decisions
A traceable record of what changed and why
Discuss the governance model
Why KeenDigit

Reasons this holds up in production.

Six delivery advantages that help business, technology, and risk leaders approve the same production system with confidence.

01 · The headline advantage

Glass box, not black box

Most AI is a black box. Every pipeline we ship carries its evidence — groundedness, eval scores, guardrail status, and the human-review trail — visible to you, always.

You see the controls, not just the output.
Eval · contract assistantLIVE
Groundedness 0.96
Eval suite 48 / 50
Guardrails PASS
Latency p95 840ms
02

We work across the whole stack

From the Linux server to the LLM. We can follow reliability, data, and access issues across the layers instead of treating the model as an isolated feature.

Server → LLMOne ownerNo seams
03

Independent by design

Vendor-neutral assurance. We audit and verify your systems — including the third parties you already rely on. No conflicts, no lock-in.

Vendor-neutralNo lock-in
04

Production-grade or it doesn't ship

Grounded, evaluated, guard-railed, human-reviewed. Eval thresholds gate every release — nothing reaches users on hope.

GroundedEvaluatedGuard-railedHuman-reviewed
05

Continuous delivery, zero-regression

Ship multiple times a week behind eval gates, with instant rollback. Improvement is constant; surprises aren't.

Eval-gatedInstant rollback3×/week
06

You keep control

Documented approvals, controlled access, structured handover. The docs, credentials, and code are yours to hold — by design.

DocsCredentialsCode — yours
Product architecture

AI is only as reliable as the stack beneath it.

AI-native applications sit at the top; data, APIs, existing software, infrastructure, and assurance make them production-ready. We work across those layers so reliability and evidence can flow up.

Reliability & evidence flow up
Layer 01 · The product

AI-Native

Intelligent apps and workflows, shipped to production with their evidence attached.

AI-Native App Development

LLM features to production — grounded, evaluated, guard-railed, human-reviewed.

Workflow AI Integration

LLMs embedded into existing workflows with audit trails and human-in-the-loop.

feeds · grounds · accelerates
Layer 02 · The substrate

AI-Ready Foundations

Clean data and reliable connectivity — what the models actually read from and write to.

API Integration

Secure auth and data mapping so your systems can talk to the model.

Database Optimization

Fast, reliable queries and backups — the data layer AI depends on.

Web Refactoring

Legacy PHP/JS modernized into a clean, AI-ready codebase.

secures · stabilizes · keeps trustworthy
Layer 03 · The bedrock

Run & Assure

The infrastructure and independent assurance that keep everything above it dependable.

Server Maintenance

Patched, tuned Linux servers the whole stack runs on.

Security Audit

Independent audit of code, infra, and vendors. Verify, don't trust.

How clients move through it

bottom-up — the path to production AI
STEP 01

Frame the workflow

Define the business outcome, operating boundary, data access, and human responsibility.

All layers
STEP 02

Prepare the foundations

Connect the data and APIs, modernize what blocks delivery, and establish controlled access.

Layer 02
STEP 03

Build & validate

Build grounded, evaluated, guard-railed AI features with human-review checkpoints.

Layer 01
STEP 04

Operate & Improve

Continuous delivery, live evals, and monitoring keep it reliable as it grows.

All layers
Company

An independent partner for production-grade AI.

Operating since 2002 and focused on enterprise AI Native since 2026 — independent, vendor-neutral, and disciplined from business approval to production operation.

KeenDigit reception
KeenDigit reception · Rawlins, Wyoming
Operating since 2002 · AI Native focus since 2026
Headquarters
Rawlins, Wyoming United States
Delivery
Global Remote-first
Language & billing
English · USD
Engagement
Independent Vendor-neutral
What we believe

Most AI is a black box. We build the kind you can put in production — grounded, evaluated, guard-railed, human-reviewed — and ship every pipeline with its evidence attached. Glass box, not black box.

It's the same discipline we apply everywhere: documented approvals, controlled access, and traceable delivery. The goal is never to dazzle you with output — it's to show you the controls behind it.

How we operate

Documented approvals

Nothing changes without a written, agreed scope. Every decision is on record.

Controlled access

Least-privilege by default. Access is granted, logged, and revoked deliberately.

Traceable delivery

Every change is attributable — what shipped, when, by whom, and why.

Structured handover

You keep the documentation, credentials, and control. No lock-in by design.

The team

Senior practitioners across the
enterprise AI stack.

An independent team organized around AI-native products, the foundations beneath them, and the controls that keep them accountable in production.

Layer 01 · Product

AI-Native practice

Grounding, evaluation, guardrails, and human review for production AI.

Layer 02 · Substrate

Foundations practice

APIs, databases, and legacy modernization — the AI-ready substrate.

Layer 03 · Bedrock

Run & Assure practice

Infrastructure and independent security audit. Vendor-neutral.

Michael Brennan
AI-Native lead
Michael Brennan
Principal, AI Engineering

Production AI — grounding, evals, and the kind of systems that ship with their evidence attached.

LLM appsEvalsGuardrails
Kevin Zhao
Foundations lead
Kevin Zhao
Lead Engineer, Platform

APIs, data, and legacy modernization — the substrate that makes AI production-ready.

APIsPostgreSQLSymfony / Vue
Simone Carter
Assurance lead
Simone Carter
Security & Audit Lead

Infrastructure, security audits, and independent vendor review.

AuditLinuxVendor review

You meet who does the work

No bait-and-switch. The senior who scopes it delivers it.

Independent & vendor-neutral

We audit third parties without conflict — including ones you already use.

You keep control

Documentation, credentials, and code stay yours. No lock-in by design.

Selected work

The evidence, not just the pitch.

Representative engagements across AI-native delivery, the foundations beneath it, and independent assurance.

Contract-review assistant
Layer 01 · AI-Native

Contract-review assistant

A grounded LLM workflow with human review in the loop — shipped to production with its evidence attached.

0.96
Groundedness
Legacy platform, modernized
Layer 02 · Foundations

Legacy platform, modernized

Legacy PHP/JS refactored into a clean, AI-ready codebase, with the data tidied behind it.

Independent security audit
Layer 03 · Run & Assure

Independent security audit

Inherited code, infrastructure, and vendors audited and stabilized before any new build began.

Evidence is useful when it travels with the release — not when it is reconstructed after the decision has already been made.
KeenDigit delivery principle
Not ready to scope the build?

Assess your path to
AI-ready production.

If the use case is clear but the systems beneath it are not, start with a scoped readiness assessment. We map the workflow, data, integrations, controls, and delivery risks before a build begins.

ScopedAI readiness assessment · defined before access

AI Production Readiness Assessment

We review the target workflow, data sources, integrations, existing applications, infrastructure, and governance needs — then document what is ready, what blocks production, and what to do first.

Assess AI readiness Discuss your AI project
Scope in writing first. Nothing is touched until you've agreed exactly what we'll do.
No access until you grant it. Least-privilege, logged, and revocable.
You keep the assessment Use the findings to plan the next step with clear priorities.
What the assessment covers
Workflow & responsibility map
Where AI acts, where people decide, and what the operating outcome requires.
Data & integration readiness
Sources, permissions, APIs, and gaps that affect grounding and delivery.
Controls & review checkpoints
Access boundaries, evaluation gates, guardrails, and human-review needs.
A prioritized delivery path
What to prepare first, what can be built next, and how rollout can be staged.
Production foundation review
Application and infrastructure constraints that could block reliable operation.
Before you ask

The questions we get first.

What business, technology, and risk leaders ask before an enterprise AI project moves forward.

Can you work with our internal team and existing vendors?
Yes. We work as an independent, vendor-neutral delivery partner, with written responsibilities and interfaces between teams. The goal is one accountable production path, not another disconnected workstream.
How do you handle enterprise data and access?
Access is granted by you, logged, and revocable, on a least-privilege basis — and nothing is touched until the scope is documented and agreed. The documentation, credentials, and code stay yours throughout. Control is the default, not an upgrade.
What does vendor-neutral mean for our architecture?
No lock-in and no kickbacks. We don't resell anyone's platform, so our assessment isn't steering you toward a product we profit from. When we audit a vendor you use, our only incentive is to report what's true.
Do we need to replace our existing systems first?
Not necessarily. We identify which systems can support the target workflow, which connections or controls are missing, and what must change before production. The build can then be staged around the systems you already rely on.
How do I know the AI you ship will actually hold up?
Every pipeline ships grounded, evaluated, guard-railed, and human-reviewed, with eval thresholds gating each release and the evidence attached. Nothing reaches your users on hope — and you can see the controls, not just the output.
What changed in 2026?
KeenDigit has operated since 2002. In 2026 we made enterprise AI Native the center of our delivery model, bringing the same full-stack engineering and structured handover discipline to grounded, evaluated, guard-railed, human-reviewed AI systems.
Ready when you are

What should your enterprise AI
do in production?

Tell us the workflow, the systems involved, and what business, technology, and risk leaders need to approve. We will use that context to frame the right first conversation.

Start with the operating outcome
Name the workflow and the decision the system needs to support.
Bring the existing stack
We account for the data, APIs, applications, and infrastructure already in place.
Make approval needs visible
Include the controls, evidence, and human responsibility your organization requires.
Rawlins, Wyoming · USA Global · remote-first
Discuss your AI project
No system access is requested here — just enough context for a focused conversation.
We use these details to review and reply to your project enquiry.

Thanks — your AI project context is with us.

A member of the delivery team will review it and reply directly.

Building enterprise AI?From use case to controlled production
Discuss your project