Document processing
Extracting and checking information from invoices, contracts, forms and emails, and entering it into your systems.
AI Automation & Implementation
Put AI to work in your day-to-day operations
Calitore helps organizations choose the right AI use cases, build them into existing workflows and systems, and put the controls in place to run them responsibly.
Common Challenges
Why AI projects stall
Getting a demo working is quick. Getting it into everyday use is where most of the effort goes.
- Pilots don't move forward. A promising prototype sits unused while questions about security, cost and ownership go unanswered.
- Use cases aren't prioritized. Effort goes to interesting ideas rather than the ones with the clearest return.
- The data isn't ready. The information AI needs is scattered across systems, inconsistent or undocumented.
- Rules are unclear. Teams don't know which tools and data they're allowed to use, so they either hold back or work around policy.
- Low adoption. Licenses are in place, but most people's daily work hasn't changed.
- Results aren't measured. Without a baseline, no one can say whether the investment is paying off.
60%
Gartner predicts that through 2026, organizations will abandon 60% of AI projects that aren't supported by AI-ready data. That's why our AI work includes the data it depends on.
Source: Gartner, February 2025Use Cases
Where AI typically helps
We focus on repetitive, rules-based and document-heavy work, with people reviewing the decisions that carry real risk.
Finance operations
Invoice matching, reconciliations, variance explanations and month-end close support.
Reporting & analytics
Automated recurring reports, written summaries of results, and plain-language questions answered from your data.
Customer service
Drafting replies for agents, answering routine requests, and routing complex cases with full context.
Sales & operations
Preparing proposals and briefs, updating CRM records, and checking orders and workflows for errors.
Internal knowledge
Secure assistants that answer employee questions from your policies, procedures and documents, with sources shown.
Services
Where we can help
01
AI Readiness & Use Case Assessment
2–3 weeks · Recommended first step- The situation
- Leadership wants an AI plan and teams have plenty of ideas, but it isn't clear which are worth funding or possible with your current data and systems.
- What we do
- Review workflows with each team, score use cases on value, effort, data readiness and risk, and assess your data, security and skills.
- You receive
- A prioritized list of use cases, a readiness summary, business cases for the top three, and a 90-day plan. Ask about an assessment
02
Pilot to Production
6–10 weeks per use case- The situation
- You've chosen a use case and need it working in the real process, connected to the systems your team already uses.
- What we do
- Design and build the automation or AI assistant, prepare the data it needs, integrate it with your systems, add human review steps, and test it against success measures agreed at the start.
- You receive
- A working solution in daily use, with monitoring, documentation, and results compared against the original baseline. Ask about a build
03
AI Governance & Adoption
4–8 weeks, then ongoing- The situation
- People are already using AI tools, and leadership needs clear rules, managed risk and broader, more consistent use.
- What we do
- Write an AI use policy, set up a simple review and approval process, define testing and oversight standards using recognized guidance such as the NIST AI Risk Management Framework, and train teams by role.
- You receive
- Clear guidelines, an inventory of AI use across the company, trained staff, and usage and impact measures leadership can track. Ask about governance
Approach
How an engagement runs
Weeks 1–3
Discover
Review workflows, estimate the benefit, check data and risk, and choose a first use case.
Weeks 3–8
Pilot
Build a working version on real data, with a success measure agreed up front, and test it with the people who will use it.
Weeks 8–14
Launch
Complete security, integration, monitoring and review steps, then move it into the live process.
Ongoing
Expand
Measure results, extend to the next use cases, and keep costs and risks under review.
How We Work
What you can expect
A measurable goal for every project
We agree on the baseline and target, such as hours saved, cycle time or error rate, before building anything.
People approve important decisions
AI prepares and checks the work; your staff sign off on anything with financial, legal or customer impact.
Independent recommendations
We recommend models and tools based on fit with your needs and existing systems. We don't resell software.
Your team owns the result
Every solution comes with documentation and training so your team can operate and extend it.
Engagement Models
Ways to work with us
Project-based
A fixed-scope assessment, pilot or build with defined deliverables and success measures.
Suited to one clearly defined use case.Retainer
Ongoing build and advisory support, delivering new use cases each quarter.
Suited to rolling AI out across several teams.Fractional AI lead
Part-time AI strategy and delivery leadership working alongside your leadership team.
Suited to companies without an in-house AI lead.Questions
Common questions
Where should we start if we're new to AI?
With the AI Readiness & Use Case Assessment. In two to three weeks you'll know which use cases are worth pursuing, what's needed to deliver them, and what to do in the next 90 days.
Which AI models and platforms do you work with?
We work across the major model providers, cloud platforms and automation tools. We usually start with what you already license and recommend changes only where there's a clear benefit.
How do you handle our data and security?
We design each solution around your security and privacy requirements, including access controls, audit logs and enterprise service terms, and we involve your IT and legal teams from the start.
How do you measure return on investment?
We record a baseline before building, such as time spent, cycle time, error rate or cost per task, and track the same measures after launch.
What happens to the people currently doing this work?
The aim is to take repetitive tasks off their plate so they can focus on work that needs their judgment. We involve them in designing the new process and provide training.
Related
Data Management
Governance, quality and documentation for the data your AI and reporting depend on.
Get Started
Start with a 30-minute conversation
Tell us about the work your teams do and where time goes. We'll point out where AI could help first, whether or not we end up working together.