Skip to content
Home/Projects/Retina
Case study · 04 / 39
Retail forecasting · Python automation

Inventory forecasts that shifted purchasing decisions.

A forecasting workflow for stock risk, margin pressure, and purchase timing, delivered within the team’s existing environment.

[ Client review ]

Talha identified what wasn't worth building and delivered the tool that shifted my purchasing decisions.

Priya Krishnan, Founder, Retina
Stylized Retina interface showing retail demand forecasting, purchase planning, and chat workflow
Select case study
CS / 04RetinaRetail forecasting · Python automation
CS / 01ThalamusDocument automation · Knowledge searchCS / 02AletheiaVoice AI · Video reviewCS / 03FRCMConstruction contracts · Review automationCS / 04RetinaRetail forecasting · Python automationCS / 05CrayoAI video · Short-form automationCS / 06MusicfyGenerative audio · Voice cloningCS / 07Just ListenAudiobooks · Subscription audioCS / 08Study PotionEducation AI · Study automationCS / 09GoMoon.aiTrading analytics · Economic calendarCS / 10RevanaAI support staff · Sales automationCS / 11TrailblazerSEO · Content growthCS / 12CoversaIQCall center AI · Agent coachingCS / 13AI Voice SystemRealtime voice · Twilio automationCS / 14Resume ScreenerRecruiting agents · OCR workflowCS / 15Document OCRHybrid search · OCR pipelinesCS / 16Credit ScoringRisk modeling · Explainable MLCS / 17Content SafetyVision AI · RecommendationsCS / 18AI Inbox TriageInbox automation · CRM routingCS / 19Invoice PO AutomationFinance extraction · Human reviewCS / 20Meeting CRM AgentSales calls · CRM updatesCS / 21Knowledge AssistantInternal documents · Cited answersCS / 22Healthcare RCM AssistantClaim review · Appeal supportCS / 23Voice Appointment SetterLead qualification · Calendar bookingCS / 24AI Quality GuardrailsPrompt QA · Safety checksCS / 25Spreadsheet DashboardSpreadsheet cleanup · KPI dashboardCS / 26Contract Change MonitorDocument comparison · Policy riskCS / 27Ad Creative GeneratorCreative testing · Ad variantsCS / 28Churn Risk PredictorCustomer health · Retention signalsCS / 29Recruiting Outreach AgentCandidate matching · Outreach draftsCS / 30Retail Shelf IntelligenceShelf monitoring · Restock alertsCS / 31CoreFit Pose CoachCore ML · Pose trackingCS / 32DefectLens QADefect detection · Human reviewCS / 33ModelOps CommandModel monitoring · Retraining alertsCS / 34PrivacyScanCore ML · Local redactionCS / 35AutoLabel StudioAI pre-labels · Human reviewCS / 36FleetCam SafetyDashcam analysis · Driver coachingCS / 37FieldVision SearchField photos · OCR snippetsCS / 38Receipt ScannerExpense capture · Local extractionCS / 39EvalForge BenchModel comparison · Regression testing
Find related work
Choose a workflowChoose a business problemStart with the kind of workflow you want to improve, then see the closest work.
AutomationAutomationsRepeatable work turned into a reliable workflow, dashboard, or internal tool.ChatbotChatbotSupport and internal assistants that answer from the right company material.PythonPython ScriptsSmall scripts that clean data, connect tools, run reports, or power a workflow.MVP SaaSMVP SaaSLean SaaS builds that prove the product, workflow, and buyer story quickly.Voice AIVoice AIVoice, audio, and conversation tools for review, routing, and decision support.DocumentsDocument ReviewContract, PDF, and knowledge-base tools that make buried details easy to act on.AI AgentsAI Agents & Workflow AutomationAgentic systems that classify work, draft actions, route tasks, and keep humans in control.AssistantsAI Assistants & Knowledge ChatAssistants that answer questions from internal context, documents, and tool data.Document AIDocument AI & Knowledge SearchParsing, extraction, OCR, comparison, and retrieval systems for document-heavy work.Voice IntelVoice AI & Conversation IntelligenceVoice, call, and meeting systems that extract next steps, signals, and follow-up actions.VisionComputer VisionAI systems that analyze images, video, screenshots, camera feeds, and inspection data.On-deviceCore ML & On-Device AIMobile AI workflows that run locally for privacy, speed, or offline use.MLOpsMLOps & AI InfrastructureMonitoring, evaluation, versioning, and operations for AI systems in production.ForecastingForecasting & Decision IntelligencePredictive systems that turn business data into risk, demand, revenue, or planning signals.RevenueGrowth & Revenue AutomationAutomation for lead routing, churn prevention, outreach, CRM updates, and sales follow-up.Creator ToolsGenerative Media & Creator ToolsCreative workflows for hooks, scripts, captions, variants, audio, and video production.Risk & EvalRisk, Compliance & AI EvaluationGuardrails, review queues, policy checks, regression tests, and risk-scored AI workflows.Data OpsData Automation & LabelingData cleanup, labeling, validation, KPI reporting, and human review workflows.Edge AIEdge AIAI workflows designed for local hardware, constrained devices, and near-source processing.Health AIHealth/Fitness AIHealth, revenue cycle, fitness, and coaching workflows with careful review boundaries.ManufacturingManufacturing AIInspection, anomaly detection, QA review, and production-floor AI workflows.
Client

Retina

Retail forecasting · Python automation

Engagement

Sprint + advisory

10 weeks build · async support

Role

AI builder

Forecasting · Python scripts · product surface

Year

2024

Q4 launch

Buyer caseinventory and planning outcomes
31%
Fewer stockouts

Across top products

18%
Margin lift

Per-product improvement

7d
Planning cadence

Weekly buying rhythm

86%
Forecast trust

Planning target hit

The business outgrew intuition‑driven buying.

Quarterly planning can’t keep pace with promotions, supplier delays, or shifting demand. Retina needs a repeatable process to choose the next purchase.

Quarterly buying couldn’t keep up with promo swings, supplier delays or demand shifts.

The team needed a repeatable decision workflow, not another report.

Each recommendation required a confidence range and a clear reason.

The workflow had to live in the tools where the team already plans and decides.

We turned forecasting into a weekly planning workflow.

The model merges sales, inventory, promotions and lead times, then sends risk flags and purchase recommendations to Slack.

  • Sales, inventory, promotions, and supplier lead times in one forecast.
  • Confidence ranges attached to each recommendation.
  • Stockout risk surfaced before buying windows closed.
  • Slack became the planning surface for weekly decisions.

Weekly cadence

Forecasting was tied to the rhythm of real buying decisions.

Slack surface

Recommendations appeared where the team already worked.

Risk flags

Stockout risk became visible before orders were placed.

Confidence ranges

Each recommendation showed enough context to support judgment.

Simple model proof

Baselines and checks kept the forecast explainable.

Decision support

The founder kept final control while the model became the planning baseline.

Week 1–2

Data audit

Reviewed sales, inventory, promotions, and supplier lead times.

Week 2–4

Clean pipeline

Built the planning table and stockout flags.

Week 4–6

Forecast tests

Compared simple baselines against stronger models.

Week 6–8

Buying logic

Added confidence ranges, risk flags, and purchase guidance.

Week 8–9

Slack workflow

Moved recommendations into the planning channel.

Week 10

Launch + tune

Shipped the workflow and adjusted it through the first cycle.

Stockout reductionInventory risk moved down across top products.
69
Margin liftBetter buying cadence improved product margins.
58
Planning speedQuarterly decisions became weekly planning.
86
Forecast trustAccuracy reached the operating target.
86
[ 01 ] Sources
Demand inputs
  • Sales
  • Ads spend
  • Inventory
  • Suppliers
[ 02 ] Prepare
Planning table
  • Promotions
  • Lead times
  • Seasonality
  • Stockout flags
[ 03 ] Predict
Forecast engine
  • Model
  • Checks
  • Ranges
  • Alerts
[ 04 ] Deliver
Buying surface
  • Slack app
  • Risk flags
  • Buy recs
  • Exports

The model mattered only after it altered the buying cadence. Slack became the product surface since the team already decides there.

Better buying rhythm: quarterly decisions became weekly planning with confidence ranges attached.

Measured inventory impact: top-product stockouts dropped 31% and margin per product improved 18% after two buying cycles.

"

Talha identified what wasn't worth building and delivered the tool that shifted my purchasing decisions.

P
Priya Krishnan, Founder, Retina
Sources
  • Sales history
  • Inventory levels
  • Promotions
  • Supplier lead times
Processing
  • Demand cleanup
  • Stockout flags
  • Seasonality
  • Product checks
Answer layer
  • Forecast ranges
  • Risk scores
  • Buy quantities
  • Planning notes
Delivery
  • Slack workflow
  • Weekly alerts
  • Exports
  • Review history
Governance
  • Human approval
  • Model checks
  • Data refreshes
  • Decision logs
Book a call

Got a problem AI might solve? Let's find out.

30 minutes. Free. No NDA needed. You leave with a clear yes-or-no on whether to build — and a one-pager you can forward to your team the same day.

[ Response ]

Within 24 hours

[ Timezone ]

GMT+5 · flexible

[ Discovery ]

Free · no NDA needed

[ Engagement ]

$1,000 / week sprint