AI implementation & integration
Your knowledge.
Your workflows.
Put AI to work.
Turn scattered information and repetitive work into a useful AI workflow. We start with your data, connect the systems you use, and define what a successful result needs to look like.
Start with one business problem and a clear scope.
A useful system starts
with your real work.
Illustrative architecture. The design follows your requirements.
What we build
Practical AI.
Built around a real problem.
Choose the work that matters. We scope the data, integrations, and testing around the people who will use it.
Company Knowledge
& Document Search
Give your team a better way to find and work with selected internal knowledge.
- Document ingestion & indexing
- Retrieval with source references
- Access requirements & answer evaluation
Workflow &
System Integration
Bring AI into a defined business process, connected to the software your team already uses.
- CRM, ERP & application integration
- Document processing & drafting
- Human approval & fallback paths
Data Readiness
& Feasibility
Understand what your data can support and what needs to change before a larger build.
- Source quality & structure
- Ownership, access & freshness
- Prioritized fixes & implementation scope
How delivery works
Proof before a bigger commitment.
A prototype gives you evidence for the next decision. Production deployment requires its own acceptance and operating plan.
01 / DISCOVER
Assess the data.
Review the workflow, available information, access requirements, and integration constraints.
02 / DEFINE
Agree what good means.
Set the scope, acceptance criteria, cost assumptions, and review points before development.
03 / BUILD
Test a focused prototype.
Build in a controlled environment and evaluate against representative tasks and failure cases.
04 / DECIDE
Earn the next step.
Use the results to decide whether to improve, deploy with an agreed operating plan, or stop.
Engagement models
A defined scope.
A clear investment.
Published engagement ranges. Final deliverables, fees, and timing are confirmed in a written proposal after discovery.
START HERE
Strategy & audit
$15,000 – $30,000
Fixed blueprint
A defined assessment of your data and systems, a technical architecture, and a prioritized implementation recommendation.
BUILD & EVALUATE
Custom MVP / PoC
$50,000 – $150,000
6–8 weeks
A scoped prototype in a sandbox environment, with agreed evaluation tasks and a recommendation for the next phase.
OPERATE & IMPROVE
Retainer / MLOps
$5,000 – $15,000
Per month · ongoing
An agreed operating scope for monitoring, performance, model or API changes, and ongoing improvements.
Built for the work
Clear expectations.
From the first conversation.
Your data sets the scope.
We review data quality, ownership, permissions, and handling requirements before selecting the implementation approach.
Your team stays involved.
Define which actions require approval, how uncertain answers are handled, and when the workflow should escalate to a person.
Success is something to test.
Choose useful acceptance criteria: answer quality, task completion, review time, latency, and operating cost for the agreed use case.
Before we build
A few useful
questions.
Do we need clean data before we start?
An initial assessment helps identify what is usable and what needs work. Data cleanup, ownership questions, and access requirements become part of the feasibility decision.
What is a company knowledge system?
It connects selected documents and records to an AI assistant through retrieval-augmented generation, often called RAG. Source references and evaluations help your team assess the answers. They do not guarantee every answer is correct.
Can AI work with our existing CRM or ERP?
We assess the available APIs, permissions, integration options, and operational requirements. The proposed scope identifies what can be connected and the limitations to account for.
Does a prototype include production deployment?
A prototype is built to test feasibility and performance in a controlled environment. Production rollout, monitoring, support, and operational acceptance are defined separately.
Start with a conversation
Where should AI make
your business work better?
Tell us about the workflow, the information involved, and what a useful result would change for your team.
A 30-minute conversation about your goals.