AI Workflows & Decision Support
We implement AI where it can save time, improve consistency or support better decisions in day-to-day operations, not where it just sounds impressive.
Where AI is actually useful
AI is most valuable when teams handle large volumes of documents, messages, requests, candidates or decisions that follow repeatable patterns. We focus on those use cases rather than broad, vague 'AI transformation' projects.
- CV, document or form information extraction
- Matching, scoring and classification workflows
- Interview or review guides generated from structured inputs
- Knowledge workflows with LLMs and retrieval
- AI layers embedded into operational automation
A practical example
For an IT staffing workflow, AI can extract candidate information from CVs, convert it into structured JSON, reformat it to the company standard, compare it with a job description, generate a match score and produce a recruiter interview guide. That is a real operational use case with immediate value.
- Less recruiter time wasted on formatting and manual review
- More consistent candidate presentation to clients
- Better pre-screening support before interviews
How we work
We evaluate whether AI is the right layer, where the decision points sit, what should remain human and what should be standardized. The goal is not to replace judgment, but to improve throughput and quality.
Business problems this solves
- Recruiters or ops staff spend hours manually reading CVs, documents or forms just to extract basic fields.
- Decisions on which candidate, lead or request to prioritize get made inconsistently depending on who's reviewing.
- Important information is buried in long documents or email threads nobody has time to fully read.
- The same judgment call gets made slightly differently every time because there's no standardized scoring.
- Teams either avoid AI entirely out of caution, or bolt on a chatbot that never touches a real workflow.
- Reviewing large volumes of similar cases — applications, contracts, tickets — becomes the bottleneck that slows everything else down.
Expected outcomes
Faster turnaround on document- and request-heavy work.
More consistent scoring and prioritization across reviewers.
Less time spent reading and re-reading the same type of document.
Clearer, more defensible reasoning behind AI-assisted decisions.
AI applied only where it adds real throughput, not as a blanket layer over everything.
Examples by function
Sales
- Inbound lead messages classified and summarized before a rep ever opens them.
- Proposal or RFP responses drafted from a structured brief, then reviewed by a rep.
- Deal notes and call summaries extracted automatically into the CRM.
Operations
- Incoming requests or tickets automatically classified by type and urgency.
- Long-form documents — contracts, SOPs, policies — summarized into a working brief.
- Repetitive review tasks pre-screened, with edge cases flagged for a person.
Recruiting
- CVs parsed and converted into structured, comparable candidate profiles.
- Candidates scored against a job description with an explainable rationale.
- Interview guides generated automatically from the candidate-to-job comparison.
Finance
- Invoices and receipts read and categorized before they hit the accounting system.
- Contract terms extracted and flagged for anything outside standard boundaries.
- Anomalies in expense or transaction data surfaced for review instead of buried in a report.
Management
- A standardized scoring model applied consistently across every reviewer, not just the most experienced one.
- Weekly summaries of open items generated automatically instead of chased down manually.
- Clear visibility into where AI made a call versus where a human did.
How it works
- 1
Trigger
A document, message, application or request enters the workflow — uploaded, emailed, or submitted through a form.
- 2
Validation
The input is checked before it reaches the AI layer: right format, required fields present, not corrupted or incomplete.
- 3
AI layer
The model extracts, classifies, scores or summarizes the content against rules and criteria defined together with you — not a generic prompt guessing at your business.
- 4
Human approval
Every AI output that leads to a real decision — hiring, pricing, contract terms — goes to a person for review before it's treated as final.
- 5
System update
Once approved, the structured result is written into your CRM, ATS, spreadsheet or ticketing system, so it fits how you already work.
- 6
Logging
Every AI decision is logged with its input and reasoning, so results can be audited, challenged, or improved over time.
Technology
Delivery methodology
01
Discovery
We understand the current process, where the real friction is, and which systems are involved.
02
Prioritization
We rank opportunities by impact and effort, and agree where to start first.
03
Design
We define the workflow, the business rules, and where a person needs to stay in the loop.
04
Prototype
We build a first working version to validate the approach before investing in the rest.
05
Integration
We connect the prototype to your real systems: CRM, spreadsheets, APIs, email.
06
UAT
Your team tests the workflow against real cases before it touches production.
07
Deployment
We publish the workflow to production, with access and permissions already scoped.
08
Monitoring
We check it performs as expected and adjust based on real usage.
Security and control
- Documents and data sent to an AI model are limited to what's actually needed for that specific task — not a bulk export of your systems.
- Personally identifiable information is minimized or redacted before reaching the model wherever the task allows it.
- AI-assisted decisions with real consequence — hiring, pricing, legal terms — always pass through a human approval step. The model informs, it does not decide alone.
- Every AI input, output and reasoning trail is logged, so a specific decision can be reviewed or challenged after the fact.
- Access to the AI layer and its outputs is scoped by role — not every team member sees every result.
- We use reputable, enterprise-grade model providers and avoid routing sensitive data through consumer-facing tools not built for business use.
- [CONFIRM: specific data-processing terms / subprocessor agreement with the chosen AI provider — this depends on which provider is selected for a given engagement and should be confirmed per client contract, not assumed generically here.]
Case Study
Coming soon
We're documenting a real case study for this service. In the meantime, tell us about your situation on a call and we'll walk through comparable examples.
Engagement model
Discovery Sprint
A short, fixed-fee engagement to map the current process, identify the best AI automation consulting opportunities, and scope what a first implementation would look like.
Fixed-Scope Implementation
A defined set of workflows built, tested and deployed against a fixed scope and price.
Retainer
Ongoing hours reserved each month for maintenance, small enhancements and new requests as they come up.
Continuous Automation Program
An ongoing partnership that keeps identifying and automating new opportunities as the business changes — not just a one-time build.
Frequently asked questions
Is this the same as just using ChatGPT? +
No. We build the AI into your actual workflow, with structured inputs and outputs, defined criteria and a human approval step — not an open-ended chat window.
What happens if the AI gets something wrong? +
Any output with real consequence goes to a person before it's treated as final, and every run is logged so mistakes can be traced and corrected.
Can this work with our existing systems? +
Yes — the AI layer reads from and writes back to your existing CRM, ATS or spreadsheets. It does not require replacing them.
How do you handle sensitive or confidential data? +
See Security & Control above: we minimize what reaches the model, log every decision, and keep humans in the loop for anything consequential.
Do we need our own data science team? +
No — we design, build and hand over the workflow with documentation, so you don't need in-house AI expertise to run it day to day.
How long does it take to see something working? +
A focused proof of concept on one document type or decision usually takes 2-4 weeks; broader rollouts are phased from there.
What if we're not sure AI is even the right fit? +
That's exactly what discovery is for — we're direct about it when a process needs redesign more than it needs AI.
Need to move faster without adding more chaos?
We can review your current process, identify the bottleneck and define the next practical step.