Engagements

Start with the right level of commitment.

Choose a discovery sprint, a focused solution build, or an empowerment program across teams. Pricing is scoped after we understand the workflow, privacy requirements, observability needs, and delivery surface.

KX-SPRINT

AI Readiness Sprint

1-2 weeks

For leaders who know AI matters but need a grounded workflow, data, observability, and empowerment map before committing to build.

Start discovery
  • Workflow and opportunity mapping
  • Private data and risk review
  • Prioritized use-case shortlist
  • Observability and model-boundary options
  • Build, train, or do-not-build recommendation
  • Executive readout
KX-ADOPT

Enterprise Adoption

Ongoing

For organizations rolling AI into multiple teams and needing empowerment, governance, model improvement, and operating support.

Plan rollout
  • Role-based AI empowerment
  • Governance and usage standards
  • Workflow portfolio support
  • Model and agent improvement cycles
  • Leadership reporting
Scoping discipline

Professional delivery starts before the proposal.

The engagement model is selected only after the operating problem is clear. That keeps the work grounded in value, privacy, implementation risk, and adoption rather than a generic package.

01

Scope before price

We estimate after the workflow, systems, data sensitivity, expected users, and delivery risk are known.

02

No generic AI package

The engagement shape depends on whether the work needs retrieval, automation, agents, model training, observability, or adoption support.

03

Evidence gates

Build work is tied to acceptance criteria, evaluation checks, and a handoff plan rather than a demo milestone.

04

Private by design

Architecture, tooling, model boundary, and operating rules are selected around your privacy and governance requirements.

Frequently asked questions

Do you require our data to leave our environment?

No. The engagement starts by defining the data boundary and selecting an architecture that fits your privacy, access, retention, and audit requirements.

Do you only build agents?

No. Agents are one pattern. We may recommend retrieval, automation, workflow redesign, model training, team training, or no AI build if the evidence does not support it.

Can you train our team?

Yes. Empowerment is part of the delivery model. We create role-specific training and playbooks so AI becomes part of daily work instead of a side tool.

When do you train a specialist model?

Only when a baseline shows that workflow design, retrieval, or prompting is not enough for the task. Model work needs evaluation data and a clear quality gate.

Can we reduce token and external API exposure?

Often, yes. We design the model and inference boundary around the use case, privacy needs, expected usage, and cost profile. That can include private deployment or specialist models where the evidence supports it.

Have one workflow in mind? We can help decide whether it needs an agent, model, automation, training program, or simpler process change.

Talk to our team