Your team doesn't need 'AI transformation.' They need the right problems solved.
We sit with your team, watch the actual work, find the bottlenecks worth fixing, and build systems simple enough that people use them from day one.
The true cost of AI is never on the invoice.
When business owners budget for AI, they add up software licenses, consulting fees, and a rough timeline. The real cost of getting it wrong runs much deeper.
The pattern is consistent: the technology was never the problem. The problem was never clearly defined before the building started.
Who we work with
CPG brands, consumer goods companies, and the supply chain, logistics, and distribution businesses that support them.
How we get paid
We charge for our work and our time. We do not accept commissions or referral fees from any tool or platform.
Where we build
Inside the tools your team already uses, with new platforms introduced only when there is no reasonable alternative.
What you own
Every system we build, every line of documentation, and every byte of operational data remains under your control.
Problems we have solved.
Route-to-market, marketing analytics, data infrastructure. A sample of what we have taken on.
Three capabilities. One integrated system.
We audit your workflows, build AI systems your team will actually use, and stay on to maintain them in the background.
We find the work worth automating.
The RAND Corporation found the #1 reason AI projects fail is that the problem was never clearly defined. Our audit fixes that before any money gets spent on implementation.
- Full workflow mapping, function by function: from demand planning to last-mile delivery
- Reliability scores with adoption risk flagged
- Honest assessment of what to skip
- Sequenced plan with effort and payback estimates
Systems your team will actually use.
We co-develop solutions with your team, not for them. Whether it’s automating PO routing, building exception-handling logic for your WMS, or connecting trade promotion data across retail portals — the done-with-you approach means your people have ownership from day one.
- Co-developed with your team, not handed off cold
- Built inside your existing platforms: ERP, WMS, TMS, EDI, or whatever you run
- Training happens during the build, not after
- Edge-case tested before production
- Full documentation at handover. You own it all
We keep it running. You keep running your business.
Your data stays in your control. We handle model updates, edge cases, and security reviews quietly in the background, so your team can focus on throughput, not troubleshooting.
- Regular performance reporting
- Background updates, no routine disruption
- High-bar data security throughout
- Direct access when something breaks
A failed AI implementation costs you a great deal more than the invoice.
Most owners frame the worst case as wasted money. The wasted money is real, but it is the smallest part.
Months of your team's time
Spent on an implementation that did not work. Time they could have spent on something that did.
Internal political capital
Burned convincing skeptical team members to try it. Capital that is harder to raise the second time.
Cultural damage
When "AI" quietly becomes a punchline in your organization after a visible failure.
Customer-facing erosion
When something feels off to the people you serve, and they move on before you understand what changed.
The initiative you'll never approve
Because the last one burned everyone involved. Often the most expensive loss of all.
Unused tools still billing you
AI subscriptions tried once and abandoned. A slow financial leak that adds up over months.
We work with a small number of companies at a time.
Most businesses that succeed with AI can point to a specific, painful workflow and say "this is eating our time." If that describes your situation, we are probably a good fit.
A small team of subject-matter experts working with a small number of clients.
There is a genuine shortage of people willing to sit with your team, watch the work, and tell you plainly which parts AI can help and which parts it should leave alone. Most businesses are not evaluating tools. They are trying to get through Tuesday. We start there.
Pixels & Clicks is a lean AI consulting practice for CPG, supply chain, and logistics businesses. We operate under Arkatra LLC, a Wyoming-registered US entity.
We focus on these industries because they face a particular combination that makes the right AI implementation genuinely valuable and the wrong one genuinely costly:
- High-volume operations with tight margins
- Multi-channel distribution and retail partner coordination
- Complex supply chain and vendor coordination
- Teams stretched thin across too many manual processes
We exist at a gap in the market. Large consultancies need large engagement sizes. Freelancers bring narrow skill sets. The mid-market CPG or logistics business ends up overbuilt by the first group or underserved by the second. We are built for that middle ground.
We never take vendor commissions.
Most consultants earn revenue from referral arrangements with the tools they recommend. We do not. If the best answer for your business is something you already own, we will tell you that.
We do not build AI to replace your team.
No AI model fully replaces an experienced professional. We build systems that hand those hours back. Your people stay. Their work improves.
We treat data security as a design constraint.
Your AI infrastructure touches sensitive operational data. We build it with the same care we would use if it were our own business: access controls, encryption, audit logs from day one.
AI Consulting and Advisory
Strategic and operational consulting for CPG, supply chain, and logistics businesses. We audit workflows end-to-end, build automation that fits inside your existing stack — from ERP and WMS to TMS and EDI — and maintain it in the background.
Three consulting engagements. Each one delivers working infrastructure.
Most AI consulting stops at strategy. Ours starts there and continues through to production.
AI Workflow Audit
We have learned not to trust what businesses say they need at the start of an engagement. Not because they are wrong, but because the real problem is almost always buried underneath the request.
An owner asks for "AI." What they actually need is to stop one person from spending eleven hours a week on a task that should take forty-five minutes.
What the audit covers:
- Every workflow that consumes meaningful team time
- Each candidate scored for reliability, adoption risk, and return
- Honest flags on workflows that look automatable but are not
- Identification of problems that do not need AI at all
- A prioritized implementation plan in plain English
What you walk away with:
- A document a non-technical executive can act on
- Honest cost and effort estimates
- A clear sequence: what to do first, second, third, and what to skip
AI Implementation & Integration
How we build:
- Inside the tools your team already uses
- New platforms only when there is no honest alternative
- Training included. Your team learns the system as we build it
- Every workflow tested against real-world edge cases
- Complete documentation at handover
What is different:
- We do not disappear after handover
- We do not bill you for tools we resell. We do not resell tools
- We do not push platforms where they do not fit
Most of what we build is not "AI" in the way people imagine it. It is workflow automation with intelligence applied where it earns its keep.
Infrastructure Maintenance & Optimization
Why this matters:
- AI models update regularly, and workflows need ongoing adjustment
- Your business evolves. What worked in Q1 may not work in Q3
- New edge cases surface that need handling
- Data security standards shift, and staying current is not optional
What we provide:
- Performance reporting with documented metrics
- Workflow adjustments in the background with no routine disruption
- Direct access when something breaks
- Ongoing security review with full audit trail
- Periodic optimization reviews
You own the data. You own the infrastructure. You can take it elsewhere at any time.
Not sure which engagement fits? Tell us where your team is stuck.
Three things we will not do.
Most consulting firms lead with what they offer. We think the more useful signal is what a firm refuses to do, because that is where their incentives show up.
No vendor commissions, ever.
Most AI consultants earn referral revenue from the tools they recommend. The buyer who already has Zapier sitting unused in a tab knows intuitively that the configuration is the work, not the idea.
- We are paid for our time and work. That is the only way
- Tool recommendations based entirely on fit
- If the best option is free or something you own, we will say so
No replacement theatre.
Nobody cares about "AI." They care about catching chargebacks before they hit, saving ten hours a week on manual data entry, or stopping inventory discrepancies from snowballing. The technology is a means. The outcome is the product.
- No AI model fully replaces an experienced professional
- We take the repetitive work off your people's plates: compliance checks, shipment tracking, demand forecasting
- Your team stays; the work that buries them goes to the machine
A limited client roster, by design.
This costs us revenue. We keep it anyway. When you are in, our SMEs are paying full attention to your business, not splitting it across twenty engagements.
- Small number of clients at any given time
- When at capacity, we say so directly
- Small team + limited roster = real focus
Three problem categories. Each one solved without disrupting how your team already works.
Client names are withheld by default. The work is documented in enough detail that you can judge whether the problem maps to yours.
Rebuilding the Delivery Math for a Rural Distribution Network
A large CPG company operated a rural distribution network built around thousands of micro-entrepreneurs: field distributors carrying products the last mile into villages and small towns.
The routes had grown organically for years without anyone modeling whether they made sense. Territory assignments were based on relationships and historical precedent. The network looked complete on a map, but delivery cycles were inconsistent and logistics costs were difficult to explain or defend.
- Audited the existing distribution logic before proposing any changes
- Applied Nearest Neighbor Optimization directly to the legacy network: same field distributors, same infrastructure, rebuilt mathematically
- Calculated the most efficient path across thousands of delivery nodes, replacing hand-drawn territory assignments with a framework that could be updated as the network changed
- Built a separate multi-distributor optimization model for a South India territory conflict where overlapping coverage was creating gaps neither team could see clearly
- Handed over documentation written for the operations team, not for a consultant
We didn't bring in a new platform or propose replacing what the company had built. The network ran on the same infrastructure it always had, just finally supported by the right math.
- Distribution cycle times improved across the rural network
- Logistics overhead came down
- Model deployed across multiple high-complexity rural markets and held up in each one
- Recognized at the CEO level as a solution that worked inside the real constraints of an existing operation
Marketing Mix Modeling and SKU Optimization Across Global CPG Portfolios
Across several large CPG portfolios in oral care, confectionery, and alcoholic beverages, the same structural problem kept appearing.
Marketing budgets had accumulated around historical patterns. Nobody had a model to say which channels were actually driving sales, or which SKUs were earning their shelf space versus quietly dragging the rest. When budgets got tight, there was nothing to cut against except last year's plan.
- Audited each client's data-capture workflows before writing a line of model code: established a single version of the truth first
- Built Marketing Mix Modeling frameworks in Python and SPSS to quantify the direct relationship between media spend and sales
- Layered in SKU-level recommendation engines to identify which products were driving category growth versus cannibalizing it
- Worked at the brand level for the confectionery portfolio across flagship chocolate and biscuit lines in India
- Delivered outputs directly to the people setting the budget, in a form they could use without a statistician in the room
3–5% promotional uplift post-reallocation. On budgets running to seven figures across multiple campaigns, that number adds up quickly.
- Capital moved from underperforming channels to the ones that were earning it
- SKU cannibalization patterns surfaced that had been invisible in aggregate dashboards
- Budget conversations shifted from historical precedent to models the team could update each cycle
From Legacy Reporting to Decisions Your Team Can Make Today
Large organizations in manufacturing and healthcare had the same gap: enormous amounts of commercial data, almost none of it usable on a timeline that mattered.
Reports the commercial team needed were sitting in legacy BI systems: QlikView, older stacks, siloed databases. They were taking two to three weeks to generate. By the time a question got answered, the business had already moved on.
- Migrated legacy BI infrastructure to Azure and GCP while keeping the core business logic intact
- Built the migration to preserve what worked: the goal was scale, not replacement
- Deployed Natural Language-to-Query tools so business users could ask questions directly, without filing a report request
- Added Computer Vision where it applied, layered on top of existing data flows
- Ran change management alongside the build: teams were trained as the system went up, not handed a manual at the end
We kept everything that worked and modernized what didn't. The team's existing knowledge of their data stayed intact, and the enterprise infrastructure the company had spent years building remained in place.
- Reporting cycles dropped from weeks to near-real-time
- Commercial teams stopped waiting on reports and started answering their own questions
- All changes were additive: nothing in the existing infrastructure was torn out
Client names are withheld throughout. Identifying details have been changed. If you are evaluating a similar problem and want to understand how we approached it, the best conversation happens on a call.
Tell us what you are working with. We will give you a straight answer.
Every inquiry is read personally. Usually within one business day.
What happens next
- We read your message personally
- If there is a fit, we schedule a 30-minute call
- On the call: no sales deck, no demo, no discovery ritual
- We ask specific questions about how your business operates
- We give an honest assessment of where AI can help, and where it cannot
- If we are not the right fit, we will tell you and try to point you somewhere useful