Auni Drives 3,500 MSMEs Signups in 3 Months with On-Device AI from M-Pesa PDFs

Auni’s rapid adoption — 3,500 business sign-ups in just three months — is more than a product milestone; it is an early indicator that practical, low-friction AI tools can move from concept labs into the everyday workflows of micro, small and medium enterprises (MSMEs). This Nairobi-built mini app, developed by Fastagger and deployed inside Safaricom’s M-Pesa Business Super App, turns routine M-Pesa PDF statements into usable, visual business intelligence using optical character recognition (OCR) and lightweight AI models, and it does so in ways that are explicitly designed for low-connectivity, affordable smartphones.

Background​

Where Auni came from and why it matters​

Fastagger, a Nairobi startup, built Auni as a practical response to a common challenge across East Africa: mobile-money platforms generate vast amounts of transactional data, but that information lives in formats — SMS threads, PDF statements, USSD logs — that are hard for small-business owners to analyze. Fastagger’s team focused on a narrow, high-impact use case: extract transaction details from M-Pesa PDFs, convert them into structured tables and dashboards, and surface simple, actionable insights — repeat customers, peak hours, geographic demand, cash-flow trends — that owners can act on without hiring an analyst. The product was launched as a mini app inside the M-Pesa Business Super App and quickly reached thousands of businesses.

The strategic platform: M-Pesa and the Super App​

Embedding Auni inside Safaricom’s M-Pesa ecosystem was a strategically decisive choice. M-Pesa remains the dominant mobile-money rails in Kenya, commanding historically around ninety percent market share and near-ubiquitous wallet penetration; the Super App is the natural distribution channel for merchant-facing mini apps and reaches millions of customers. That distribution, combined with the operational reality that many merchants already rely on PDF transaction statements for bookkeeping, created an unusually low-friction adoption path for Auni. Recent market reads put M-Pesa’s market share near 90–91%, though that number has been slowly shifting as competitors gain ground — a dynamic worth watching.

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What Auni does (technical overview)​

Core functions​

• PDF ingestion and OCR: Auni ingests M-Pesa PDF statements and uses OCR to extract transactional rows — payer, payee, amount, date, time and reference fields.
• Table reconstruction: Extracted text is normalized and organized into structured tables, resolving common OCR errors and aligning fragmented fields.
• On-device and lightweight inference: Models and heuristics are optimized to run on low-memory Android devices so that core parsing and initial analytics are available even with intermittent connectivity.
• Dashboards and alerts: The app presents simple dashboards and prebuilt insights (top customers, busiest hours, cash-flow summaries) that are readable without technical training.
• Planned generative features: Fastagger intends to add natural-language question answering so users can ask things like “Which days in the last month had the highest repeat-customer rate?” and receive instant summaries.

How it’s built: the practical stack​

Fastagger has emphasized an architecture that mixes cloud and on-device components. The OCR and table-reconstruction pipeline is complemented by cloud services for heavier model updates and cross-account aggregation, while core parsers and inference components are pruned to function on modest hardware. Microsoft Azure provides cloud infrastructure and tools used during development and scaling. Fastagger also leverages open tools (for example, Table Transformer models in early reports) to accelerate document structure extraction. The result is a hybrid approach: local, responsive functionality for immediate insights and cloud-based capabilities for model improvement, backups and optional generative features.

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The adoption story: 3,500 businesses in three months​

The headline metric and what it represents​

Fastagger reports that Auni onboarded roughly 3,500 businesses in its first three months after being embedded into the M-Pesa Business Super App. That metric is notable for two reasons: first, the rate of sign-ups demonstrates a clear product-market fit among merchants who already use digital payments; second, it shows how platform-level distribution (Safaricom’s Super App) can dramatically speed adoption of developer-built mini apps. The figure has been cited in several recent features and Fastagger’s own communications.

Early user outcomes — compact case studies​

Reported user stories from Nairobi include retail and services businesses observing tangible, short-term benefits:

• A cosmetics and grooming retailer used repeated-customer and geographic demand insights to refine delivery routes and negotiate better in-store placement. The founder said data “moved us from guessing to knowing.”
• A hair salon owner began applying customer-frequency analytics to design targeted discounts and optimize staffing, which the owner linked to stronger revenues and a recent expansion.
  These testimonials illustrate one of Auni’s central value propositions: unlock everyday transactional intelligence for merchants who lack accounting teams or analytics staff.

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Why this model is compelling for emerging markets​

Accessibility by design​

Auni’s success highlights three practical constraints that many African markets impose on tech products — and how design choices can overcome them:

• Device limitations: Many users rely on entry-level Android phones with limited RAM and storage. Auni’s on-device optimizations make run-time experience acceptable on those devices.
• Intermittent connectivity: Offline-capable features and local parsing reduce dependency on continuous high-speed data.
• Familiar workflows: By working with artifacts merchants already have (M-Pesa PDFs), the app avoids asking users to change long-established administrative behaviors.

Platform distribution​

Embedding as a mini app in the M-Pesa Business Super App gives Auni:

• Immediate trust-transfer from Safaricom’s brand,
• Lower discovery friction for merchants, and
• Simpler on-boarding because the M-Pesa user base already owns and uses the host app.
  This alignment of technical design and distribution channel is a textbook example of “product + platform” synergy.

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Strengths and strategic advantages​

• Democratizes business intelligence: By turning payment statements into visual summaries, Auni gives non-technical business owners insights previously available only to firms that could hire analysts.
• Distribution leverage: The M-Pesa embed dramatically lowers acquisition cost and scales initial trials rapidly.
• Local-first engineering: On-device inference and offline capabilities match the infrastructural realities of many African merchants.
• Cost-effectiveness: For many MSMEs that cannot afford consulting or bookkeeping staff, a low-cost app that surfaces customer retention trends or cash-flow risk is high-value.
• Tangible ROI pathway: Small improvements in customer retention or reduced stockouts can have outsized profit effects for small businesses, making the app’s value proposition easy to explain and measure.

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Risks, limitations and open questions​

No technology that touches financial data is without trade-offs. Below I outline the principal risks and practical weaknesses decision-makers should consider.

1) Data privacy and consent​

Auni parses sensitive transactional records that include payer identifiers and amounts. Embedding inside a telco-hosted super app may simplify onboarding, but it also concentrates sensitive flows in a provider-controlled environment. Questions that need explicit answers include:

• What exact permissions are requested from merchants?
• Where and how long is extracted data stored?
• Is personal data pseudonymized or encrypted at rest and in transit?
  Absent clear, merchant-facing privacy guarantees, trust can degrade quickly. Any operator handling transaction-level data should publish and enforce transparent data governance policies and allow merchants to export and delete their data on-demand.

2) OCR and extraction accuracy​

M-Pesa statements can vary by format, and OCR errors are common when documents are low-resolution, poorly scanned, or include unusual fonts. Inaccurate extraction leads to bad dashboards and bad decisions. Practical considerations:

• Error rates must be the subject of ongoing measurement and public metrics.
• There should be an easy correction workflow for merchants to fix mis-parsed rows (human-in-the-loop).
• Confidence scores should be surfaced in the UI so users understand uncertainty.
  If Auni fails silently, merchants may distrust the tool; if it surfaces noisy data clearly and enables fixes, it can become a reliable assistant.

3) Vendor and platform lock-in​

Embedding in the M-Pesa Business Super App confers distribution advantages but creates dependence on Safaricom’s policies and platform evolution. This dependence raises questions:

• If Safaricom changes mini app rules, what happens to Auni?
• Can merchants use Auni outside the Super App environment, or export their datasets to other accounting systems?
• How does Fastagger handle portability and data ownership?
  Mitigations include open export formats, clear contractual terms with platform partners, and multi-channel distribution beyond a single super app.

4) Competition and incumbency​

Large payment providers, banks or regional fintechs could build similar parsing and analytics features and bundle them more tightly into merchant accounts. Additionally, the marginal cost of adding such features for big players may be low once the distribution is solved. Fastagger’s defensibility will depend on product polish, local market knowledge and the quality of integrations rather than on a single early sign-up metric.

5) Regulatory and compliance risk​

Parsing transaction-level data could draw regulatory scrutiny, particularly around Know Your Customer (KYC), anti-money laundering (AML) and data protection requirements. Fastagger — and any merchant using the app to categorize or flag suspicious transactions — should ensure alignment with Kenyan data-protection laws and financial regulations. Clear audit logs and role-based access controls will be critical.

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Practical recommendations (for merchants, investors and platform operators)​

For merchants evaluating Auni​

• Start with a pilot: Try the tool on a single business line or a single month of statements to validate extraction accuracy.
• Verify exports: Ensure you can export raw parsed transactions to your accounting package or a CSV for backup.
• Validate decisions: Use the insights to test low-cost operational changes (hours, promotions, staffing) for a month before making large investments.
• Guard sensitive data: Confirm what Fastagger stores and request deletion of historical data if you stop using the service.

For Fastagger and product teams​

• Publish an explicit data-privacy and retention policy, and bake in merchant-controlled data deletion and export functionality.
• Invest in explainability: surface confidence scores and provide an easy correction interface for parsed transactions.
• Expand distribution strategies beyond a single platform to reduce single-point risk; explore partnerships with accounting packages and merchant services.
• Regularly publish performance metrics (e.g., OCR accuracy by statement type) to build trust.

For platform partners and telcos​

• Treat mini-app developers as strategic partners and set clear SLAs around uptime, data portability and deprecation windows.
• Encourage multi-stakeholder audits and security reviews for apps that process payment data.
• Provide sandbox environments that allow startups to validate parsing across different statement formats without exposing live customer data.

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The potential economic impact​

MSMEs are the backbone of many economies: global estimates place small and medium enterprises at roughly 90% of businesses worldwide, contributing a majority of employment in many countries and driving a substantial share of GDP. In developing economies, growing MSME productivity can unlock large job and income effects. Tools like Auni — low-cost analytics that increase customer retention or reduce stockouts — have a plausible pathway to meaningful impact if they scale. For example, even modest improvements in customer retention (a small single-digit percentage) can produce outsized profitability improvements for microbusinesses, which compounds into broader economic resilience.

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Charting what comes next: feature roadmap and scaling signals​

Short-term product bets that matter​

• Natural-language queries: Allow merchants to ask questions in plain language and get synthesized insights (the feature Fastagger plans to add).
• Automated alerts: Push alerts for unusual cash-flow swings, declining repeat-customer rates or sudden payment anomalies.
• Integrations: One-click exports to common accounting systems and CSV exports for tax filing.

Scaling indicators to watch​

• Retention and active usage: Sign-ups are necessary but not sufficient. Watch daily/weekly active usage and the percentage of merchants who act on insights.
• Revenue per merchant: Are merchants willing to pay for premium analytics or integrations?
• Data quality metrics: Measurable OCR accuracy and correction rates across statement formats.
• Platform independence: Adoption outside the M-Pesa Super App will indicate whether the product stands on its own or is primarily a platform-enabled viral play.

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Final assessment: why Auni matters — and what will determine its long-term success​

Auni’s first-quarter trajectory demonstrates three important realities: there is an urgent market appetite among MSMEs for simple, actionable analytics; distribution through dominant local platforms can accelerate adoption dramatically; and careful product engineering that respects device and connectivity constraints is essential to reach last-mile users.
That said, the app’s long-term impact will depend on credible answers to questions about data governance, extraction accuracy, platform dependence and the ability to convert sign-ups into sustained, revenue-generating usage. If Fastagger can sustain product quality, demonstrate transparent privacy and governance practices, and broaden its go-to-market beyond a single distribution channel, Auni could be a replicable model for how locally built, globally powered AI tools deliver measurable productivity gains across low-resource markets.
In short: Auni is not just an app — it is a case study in how to design for the constraints of emerging-market commerce, and a reminder that democratizing analytics begins with solving the mundane but universal problem of turning transaction logs into clarity. The next year will reveal whether early momentum becomes durable transformation for thousands of small businesses.

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Source: CIO Africa Microsoft-Powered AI App Auni Hits 3,500 Business Sign-Ups In Three Months

 

[ChatGPT]

Auni’s rapid climb to 3,500 business sign-ups inside three months is a clear signal that Kenyan micro, small and medium enterprises (MSMEs) are not just curious about AI — they’re ready to use it as part of their everyday commerce. The Nairobi startup Fastagger built Auni as a lightweight, Microsoft Azure–backed mini app inside Safaricom’s M-Pesa Business Super App. Auni turns M-Pesa PDF statements into structured dashboards with on-device optical character recognition (OCR) and pruned AI models, delivering immediate sales, customer and cash-flow insights to entrepreneurs who otherwise lack analytics teams or accounting systems. Early adopters report concrete operational wins — faster inventory and staffing decisions, better-targeted promotions, and clearer negotiation positions with retailers — all from analytics extracted automatically from digital receipts.

Background​

Kenya’s mobile-money ecosystem has created one of the world’s most data-rich small-business environments: millions of daily transactions generate transactional logs that are plentiful but often unusable for MSMEs because they live in PDF statements or fragmented records. Safaricom’s M-Pesa Super App — which hosts mini apps and reaches tens of millions of users — provided the distribution channel. Fastagger embedded Auni into the M-Pesa Business Super App to remove friction: merchants simply allow the mini app to read their M-Pesa PDFs, and Auni returns dashboards that highlight repeat customers, peak selling hours, geography-based demand and simple cash-flow analyses. That product-market fit, combined with platform-level distribution, helped Auni reach 3,500 sign-ups in its first three months.
Why this matters now

• Mobile money still dominates Kenya’s payments landscape: Safaricom’s M-Pesa held roughly nine in ten mobile-money subscriptions in recent quarterly data, leaving a narrow gap for competitors. That scale means M-Pesa transaction records are a near-universal source of business intelligence for Kenyan MSMEs.
• MSMEs account for an estimated 90 percent of businesses and roughly 70 percent of employment across Africa; small percentage improvements in customer retention or operational efficiency scale into meaningful local economic impact.

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What Auni does — product and user value​

From PDF statements to actionable dashboards​

Auni’s core proposition is deceptively simple: ingest M-Pesa PDF statements and output structured, digestible analytics. The app’s pipeline typically involves:

• OCR that recognizes text in M-Pesa statements (dates, phone numbers, transaction narratives).
• Table reconstruction to transform messed-up or multi-line PDF rows into clean tabular records.
• Lightweight on-device inference for immediate analytics (sales by hour, top customers, repeat purchase rates).
• Optional cloud components for heavier aggregation, model updates and backups.

The result: a merchant can see spike hours, repeat-customer lists and geographic demand maps without manual data entry or spreadsheets. For many users that shifts decisions from intuition to measurable actions: staffing, delivery routing, promotions and stock reordering. Early users like Njoki Njoroge of Mandevu Beard Care and Peter Chege of Master Stylists underscore the practical gains — clearer restocking cadence, smarter discounts, and staff scheduling based on hourly revenue patterns.

Key features and user benefits​

• Instant sales and transaction summaries extracted from M-Pesa PDFs.
• Repeat-customer tracking for retention-focused promotions.
• Time-of-day and day-of-week peak insights to optimize staffing and deliveries.
• Simple, visual dashboards designed for non-technical users on affordable phones.
• Plans for natural language querying and generative explanations so merchants can “ask” about performance in plain language.

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Technology stack and design choices​

Hybrid on-device plus cloud architecture​

Fastagger adopted a hybrid approach: core OCR and parsing run on device or in the mini app container to support offline or low-connectivity use, while Azure cloud services handle heavier model updates, aggregation and optional generative features. This reduces latency and data egress costs, while allowing the company to improve models centrally. The approach also keeps essential functionality available on older, affordable smartphones — an explicit design goal given device realities in the market.

Models, libraries and platform partners​

• OCR and table-reconstruction pipelines rely on a mix of open-source tools (Table Transformer models and similar libraries) and bespoke pruning to reduce compute and memory footprint.
• Auni uses Microsoft Azure for cloud infrastructure, and integrates smaller language-model components (reported to include Phi family small LLMs in some deployments) and on-device inference frameworks such as ONNX and lightweight runtimes to run on the edge.

Why on-device processing matters​

• Performance: immediate extraction without round-trip cloud latency.
• Reliability: continued operation even when mobile data is patchy or absent.
• Cost: lower operational costs for merchants and the startup by minimizing cloud compute.
• Privacy surface reduction: keeping parsed data on-device reduces the amount of transaction data sent to remote servers, but it does not eliminate cloud-resident analytics for backups or model improvements (opt-in designs vary).

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Adoption, scale and real-world impact​

The 3,500 sign-ups milestone​

Fastagger reports that Auni reached roughly 3,500 business sign-ups in the first three months after being embedded into the M-Pesa Business Super App. That figure has been included in multiple accounts of the launch and is consistent with the product’s high-touch distribution inside a platform that already reaches millions. Independent coverage and Fastagger’s own messaging describe a similar trajectory of rapid uptake, with earlier pilot months showing several hundred merchants in the first 30 days. While company-provided metrics should always be read with standard journalistic caution, the adoption trend is corroborated across reporting outlets. (news.microsoft.com)

Illustrative case studies​

• Mandevu Beard Care (Nairobi): The founder says Auni helped convert PDF statements into customer maps and repeat-purchase lists, enabling targeted reminders and smarter retail partnerships. That clarity assisted inventory planning and improved delivery logistics during peak hours.
• Master Stylists Hair Salon and Barbershop (Nairobi): The salon used insights on customer frequency and spending to design targeted discounts, and to staff the shop during peak periods; the owner attributes part of a recent expansion to the improved operational visibility.

These are not just feel-good anecdotes: the unit economics of a small retailer are sensitive to small gains. Research cited by practitioners suggests that modest improvements in retention and repeat-purchase rates can produce outsized profit gains for MSMEs; connecting that outcome to scalable tools is the core promise of Auni.

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Market context and competition​

Why M-Pesa integration is strategic​

Safaricom’s M-Pesa remains the dominant rails for person-to-person and merchant payments in Kenya. Even as competitors like Airtel Money have claimed incremental market-share gains in recent quarters, M-Pesa’s ubiquity means that merchant transaction histories are nearly universal in coverage — a natural single-source dataset for business intelligence products aimed at MSMEs. For any analytics solution to scale in Kenya, tight integration with M-Pesa is a near requirement.

Competitive pressures and the mini-app ecosystem​

• Platform distribution is a force multiplier: embedding inside the M-Pesa mini-app ecosystem dramatically lowers acquisition cost compared with standalone mobile apps.
• But platform dependence brings concentration risk: changes to the M-Pesa API, PDF formatting, or mini-app policy could materially affect Auni’s functionality or distribution.
• Regional competition: similar mobile-money analytics tools — whether from banks, fintech startups or telcos — may target the same merchant segment. Auni’s advantage so far is the combination of distribution (via Safaricom) and localized product design for low-resource devices.

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Strengths: Why Auni’s approach works​

• Product-market fit through platform distribution: shipping inside the M-Pesa Business Super App gives immediate, relevant reach to merchants who already live in that ecosystem.
• Local-first design: optimization for older Android phones and low-connectivity environments addresses the real constraints faced by many Kenyan MSMEs.
• Practical analytics focus: Auni focuses on a small set of high-value signals (repeat customers, peak hours, geography), which are easy to act on and deliver immediate return.
• Hybrid architecture: on-device parsing plus cloud improvements balance responsiveness and long-term learning, while keeping operational costs practical for an early-stage startup.

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Risks, limitations and cautionary notes​

Data quality and OCR limits​

OCR and table-reconstruction from commodity PDFs are inherently imperfect. Formatting changes to M-Pesa statements, handwriting within scanned attachments, or inconsistent narrative fields can produce errors: misattributed transactions, missed rows, or garbled phone numbers. That, in turn, yields noisy analytics unless the product includes robust verification and correction workflows. Merchants relying blindly on automated dashboards risk making decisions from incorrect data.

Platform and vendor dependence​

Auni’s success is strongly coupled to Safaricom’s mini-app strategy. Policy shifts, new developer requirements, or even commercial re-pricing for mini-app distribution could change economics or availability. Additionally, if Safaricom adjusts the PDF statement format or the way statements are delivered to users, OCR pipelines must adapt quickly.

Privacy, consent and regulatory risk​

Converting payment statements into analytics raises legitimate privacy questions. Key considerations:

• Explicit consent and transparency: merchants need clear information about what data is parsed, how long it’s stored, and whether raw transaction details leave their device.
• Data residency and compliance: if backups or aggregated model training data are held in Azure clouds, Fastagger must clearly document where data resides and which protections apply, especially under evolving East African data-protection norms.
• Attack surface: mini apps add new vectors for social engineering and malware, especially given the prevalence of M-Pesa scams. Any app that interacts with transaction data must follow secure code practices, robust authentication, and least-privilege data access.

Model bias and interpretability​

When models make recommendations (e.g., “promote product X”), merchants require clear explanations. Black-box outputs without confidence scores or traceability may be misinterpreted, especially by non-technical users. Generative features — proposed for Auni — magnify this need for guardrails, safety layers and conservative defaults.

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Practical guidance and recommended best practices​

If you’re a small business considering Auni or similar AI-enabled tools, here are practical, sequential steps to reduce risk and maximize value:

• Confirm consent and data flows
• Understand exactly what the app reads from your M-Pesa statements.
• Choose settings (if available) to keep raw statements local and allow only aggregated analytics to be uploaded.
• Validate outputs before acting
• For the first 4–8 weeks, cross-check Auni’s transaction extracts against manual records for a sample of days.
• Spot-check phone numbers and amounts to measure OCR accuracy.
• Keep backups and export capabilities
• Export monthly CSVs of parsed data and store them locally or on trusted cloud storage for audits and reconciliation.
• Use insights as advisors, not dictators
• Treat Auni’s recommendations as input to decisions. Combine them with domain knowledge (seasonality, supplier lead times, local events).
• Stay security-conscious
• Ensure your Safaricom account PIN and phone security (biometrics, device lock) are current.
• Be wary of phishing that tries to replicate mini-app prompts or requests for PINs.
• Monitor costs and scale deliberately
• Track any fees or limits associated with mini-app use and Azure-backed features; evaluate ROI before scaling promotions or expansion decisions.
• Engage the vendor on privacy
• Ask Fastagger (or the mini-app provider) for a plain-language privacy and data-handling summary, including retention timelines and deletion policies.

These steps protect merchants from over-reliance on noisy data and help them capture real operational wins.

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What success looks like — metrics to watch​

For merchant-level assessment, track:

• Repeat-customer rate (30/60/90-day windows): increasing retention is more valuable than one-off sales.
• Revenue per peak-hour vs off-peak: helps optimize staffing and promos.
• Order fulfillment lead time: improved mapping should reduce delivery gaps.
• Error rate in parsed transactions: percentage of transactions requiring manual correction.

For Fastagger and platform partners, early success indicators will include:

• Activation-to-engagement conversion (how many sign-ups become daily users).
• Average actions taken per merchant per week (e.g., promotions created, route changes).
• Retention rates and churn after the first 90 days.
  These measurable signals will determine whether the product genuinely improves merchant economics or just provides a nice-to-have dashboard.

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Looking ahead — product roadmap and ecosystem implications​

Fastagger has signaled plans to expand Auni’s sector focus beyond retail and salons into healthcare, manufacturing and agriculture, and to introduce generative AI features that let merchants ask questions in natural language and receive narrative answers. That roadmap raises both opportunity and responsibility: generative features must be carefully calibrated to prevent hallucinations and to remain conservative about financial guidance.
From a broader systems perspective, Auni’s model — a local startup building on global cloud infrastructure and platform distribution — is a repeatable pattern across Africa: localized user experience, edge-optimized models, and cloud-backed learning loops. If the approach proves durable, we can expect:

• More sector-specialized mini apps appearing inside mobile-money platforms.
• Increased demand for standardized, machine-readable transaction exports from telcos and banks.
• Pressure on regulators and platforms to standardize privacy and developer policies for mini apps.

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Final assessment​

Auni’s early traction — 3,500 sign-ups in three months — is meaningful not because the number itself is dramatic, but because it validates a product-market fit in a market where mobile-money records are nearly universal. The app’s strengths are practical: local-first design, hybrid on-device/cloud architecture, and a laser focus on a handful of high-impact merchant metrics. These characteristics make Auni a credible tool for merchants that previously could not afford analytics.
That said, success at scale will hinge on execution around data quality, privacy, resilience to platform changes and clear guardrails for generative features. The underlying technical stack (OCR, table transformers, Azure and lightweight edge runtimes) is well-chosen for the use case, but it comes with the usual caveats: OCR errors, model drift, platform dependency and regulatory scrutiny. Fastagger will need to maintain tight customer support loops, transparent privacy practices, and rapid adaptability to changes in M-Pesa outputs or Safaricom policies.
For Kenyan MSMEs, Auni represents an important shift: the democratization of simple, actionable analytics delivered inside the same mobile environment where most commerce already happens. If Fastagger can sustain product reliability, protect merchant data, and keep features understandable and auditable, Auni and solutions like it could become part of the backbone that helps small businesses move from instinct-driven decisions to repeatable, data-informed operations — a small but crucial step toward building more resilient local economies.

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Source: Soko Directory Microsoft-Powered AI App Auni Surpasses 3,500 Business Sign-ups in Three Months