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Artificial Intelligence

AI Consulting

From a clear strategy to systems in production — the shortest line between ambition and outcome.

TL;DR

Klamka Group advises leadership teams on where artificial intelligence creates real value, then builds and governs the systems that deliver it. We start with an honest assessment, prioritize by return rather than novelty, and stay through deployment. Engagements run from a focused opportunity audit to a full roadmap with our engineers executing alongside your team.

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Overview

What this service delivers

AI consulting is the discipline of deciding what to build, what to ignore, and in what order — before a single model is trained. Klamka Group brings that judgment to organizations that want measurable results from artificial intelligence rather than experiments that stall after the pilot.

It is built for executives and operators carrying real accountability: a managing director weighing where to commit budget, an operations lead drowning in manual process, a founder who needs an AI roadmap their board will fund. We work across e-commerce, finance, healthcare, manufacturing, logistics, hospitality, and professional services, with clients on several continents.

We deliver differently because we own the outcome end to end. The same house that maps your strategy also engineers the deployment, so recommendations are grounded in what can actually ship. We assess candidly, quantify the case in your numbers, and remain through production, measurement, and the handover that lets your team carry it forward.

What's included

Inside the engagement

AI Opportunity Audit

A structured review of your workflows, data, and systems that surfaces where AI returns the most, scores each candidate by value and feasibility, and rules out the work that will not pay back.

AI Strategy & Roadmap

A sequenced plan tied to business objectives — priorities, dependencies, budget, and timeline — written so a board can fund it and an operations team can act on it.

Data & Infrastructure Readiness

An assessment of whether your data, pipelines, and platforms can support the intended systems, with a concrete remediation path before any model is built.

Solution Architecture & Vendor Selection

Independent design of the right approach — build, buy, or fine-tune — and impartial selection among models, platforms, and providers based on fit rather than fashion.

Governance, Risk & Compliance

Frameworks for responsible use: data privacy, model oversight, audit trails, and human-in-the-loop controls aligned to your regulatory environment.

Proof of Concept & Production Deployment

Rapid validation of the highest-priority use case, then engineering it into a live, monitored system — with knowledge transfer so your team owns what we leave behind.

Proof

Where it delivers

Representative engagements — the problem with the old way, what we rolled out, and the estimated result. Company names are illustrative.

Meridian AtelierE-commerce (fashion)
Catalog production time cut ~90% The old way

A merchandising team of nine wrote every product description and tagged every catalog image by hand, taking three days per seasonal drop and leaving thousands of older listings thin and unsearchable.

 What we rolled out

We audited the catalog workflow, then designed and deployed a generative pipeline that drafts on-brand descriptions and auto-tags imagery, with human review retained on hero products.

 Estimated result

Catalog turnaround fell from three days to under four hours, and on-site search conversion on older inventory recovered noticeably as listings became findable.
Norland Precision ComponentsManufacturing
~35% less unplanned downtime The old way

Unplanned breakdowns on two CNC lines were caught only after failure, costing roughly four unscheduled stoppages a month and overtime to recover schedule.

 What we rolled out

We assessed sensor and maintenance-log data, confirmed it was sufficient, and built a predictive-maintenance model that flags degradation early and routes alerts to the floor supervisor.

 Estimated result

Most failures are now caught days ahead, cutting unplanned downtime by about a third and smoothing the maintenance calendar.
Crestline MutualFinance (insurance)
First-response time down from 5 days to under 1 The old way

Claims triage was fully manual; adjusters read every submission to assess complexity, creating a five-day backlog and inconsistent routing across the team.

 What we rolled out

We designed a triage model that classifies and prioritizes incoming claims, plus a governance layer with audit trails to satisfy the compliance team before launch.

 Estimated result

Straightforward claims now route automatically, freeing adjusters for complex cases and shortening the average first-touch time.
Sawasdee Riverside CollectionHospitality (hotels)
Direct-booking inquiries answered 24/7, ~60% handled automatically The old way

A small reservations team answered the same guest questions across email and chat at all hours, missing inquiries overnight and losing direct bookings to OTAs.

 What we rolled out

We mapped the inquiry mix and deployed a multilingual concierge assistant trained on property policies, with seamless handoff to staff for anything sensitive.

 Estimated result

Most routine questions are now answered instantly in the guest's language, lifting captured direct bookings and easing the night shift.
Aldgate & Pierce LLPProfessional services (legal)
~70% faster first-pass review The old way

Associates spent hours per matter manually reviewing contracts for non-standard clauses, a slow and uneven process that exposed the firm to missed risks.

 What we rolled out

We ran a focused proof of concept on clause extraction, then deployed a review assistant that highlights deviations from the firm's playbook with citations back to source.

 Estimated result

First-pass contract review now takes a fraction of the time, with partners reviewing flagged items rather than full documents.
Helio Grid EnergyEnergy (renewables)
Forecast error reduced ~28% The old way

Solar output forecasting relied on spreadsheets and a forecaster's intuition, producing imprecise day-ahead bids and recurring penalties on the balancing market.

 What we rolled out

We assessed weather and generation data readiness, then built a forecasting model integrated into their bidding workflow with clear confidence ranges.

 Estimated result

Day-ahead forecast accuracy improved enough to materially reduce balancing penalties and steady revenue.
Verda LogisticsLogistics & freight
Fuel and mileage down ~18% The old way

Dispatchers planned next-day routes manually each evening, a two-hour exercise that left trucks under-loaded and added avoidable mileage.

 What we rolled out

We designed a route-optimization system that ingests orders, vehicle constraints, and traffic patterns to propose plans dispatchers approve and adjust.

 Estimated result

Planning collapsed to minutes and consolidated loads trimmed total mileage, lowering fuel spend across the fleet.
Banyan Health NetworkHealthcare
No-show rate reduced ~22% The old way

Clinic call-center staff spent most of their day on appointment scheduling and reminders, yet the no-show rate stayed high and lines were often busy.

 What we rolled out

We built a scheduling and reminder assistant with strict privacy controls and human escalation for clinical questions, deployed under a governance framework we co-authored with their compliance lead.

 Estimated result

Routine scheduling moved off the phones and proactive reminders cut no-shows, recovering otherwise-lost appointment slots.
Lumen Cloud SystemsSaaS
Ticket deflection ~40%, response time halved The old way

A growing support queue meant tier-one agents answered repetitive questions while complex tickets waited, and response times slipped against the SLA.

 What we rolled out

We deployed a retrieval assistant grounded in their documentation that drafts agent replies and deflects common questions in-product, with answers traceable to source articles.

 Estimated result

Ticket deflection rose and agents resolved the remaining volume faster, bringing SLA compliance back within target.
Anchor Bay Realty GroupReal estate
Lead-to-viewing rate up ~31% The old way

Agents qualified inbound leads by hand and followed up inconsistently, letting warm prospects go cold and skewing the team toward whoever shouted loudest.

 What we rolled out

We built a lead-scoring and nurture system that prioritizes inquiries by intent and drafts tailored follow-ups for agents to send.

 Estimated result

Agents now spend their time on the highest-intent leads, improving the share of inquiries that reach a viewing.
Polaris Academy OnlineEducation (e-learning)
Feedback turnaround from 8 days to under 24 hours The old way

Instructors graded short-answer assessments and wrote individual feedback by hand, capping class sizes and delaying results by over a week.

 What we rolled out

We piloted automated feedback on a single course, then rolled out an assistant that drafts personalized feedback for instructor review across the catalog.

 Estimated result

Feedback now reaches learners within a day while instructors retain final say, letting the platform scale enrollment without proportional staffing.
Brightfold Retail GroupRetail (grocery)
Perishable waste down ~24%, stockouts down ~19% The old way

Store-level inventory ordering was driven by a manager's experience, producing frequent stockouts on fast movers and waste on perishables.

 What we rolled out

We assessed point-of-sale and supplier data, then deployed a demand-forecasting model that recommends order quantities per store and category.

 Estimated result

Stockouts on key lines eased and perishable waste dropped, improving both availability and margin across pilot stores.
Kestrel Wealth PartnersFinance (wealth management)
Reporting effort cut ~85% The old way

Advisors assembled quarterly client reports by stitching data from several systems by hand, burning two days per cycle and risking transcription errors.

 What we rolled out

We designed an automated reporting pipeline that consolidates portfolio data and drafts narrative commentary advisors finalize, with every figure traceable.

 Estimated result

Report assembly became near-instant, giving advisors back days each quarter to spend with clients rather than spreadsheets.
Questions

Frequently asked

How is AI consulting different from simply hiring a developer to build a model?+
A developer builds what you ask for; consulting decides what is worth building. We start by quantifying where AI returns the most in your specific operation, then sequence the work so budget goes to outcomes rather than experiments. Because our engineers also deliver, the strategy is grounded in what can actually ship.
What does a typical engagement look like, and how long does it take?+
Most clients begin with an opportunity audit lasting two to four weeks, producing a prioritized roadmap. From there, a focused proof of concept usually takes four to eight weeks, and full production deployment depends on scope. We size each phase to your readiness rather than a fixed package.
Our data is messy and scattered. Are we even ready for AI?+
Most organizations are less ready than they hope and more capable than they fear. Part of our assessment is an honest read on your data and infrastructure, with a concrete remediation path before any model is built. If the foundations need work, we tell you plainly and address them first.
How do you handle privacy, compliance, and the risk of AI making mistakes?+
Governance is part of every engagement, not an afterthought. We design data-privacy controls, audit trails, and human-in-the-loop checkpoints aligned to your regulatory environment, and we keep people in control of consequential decisions. The aim is systems you can defend to a regulator and trust in production.
What happens after deployment — are we left to maintain it alone?+
We build for handover. Deployment includes monitoring, documentation, and knowledge transfer so your team can operate and extend the system independently. Many clients keep us on for ongoing advisory, but that is a choice, not a dependency we engineer in.
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Related disciplines

Full AI Services→AI Integration→AI Education→Advanced Technological Solutions→All services→

Tell us where the manual work hurts most, and we will show you, in your own numbers, what intelligent automation could return — start the conversation with Klamka Group.

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