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Problem & solution reads
Problem & solution posts on Yomu, summarised newest first.
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- 20 companies
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Problem & solution posts
How CFOs in consulting can recover margin with Databricks
Consulting firms are facing margin pressure despite growing deal pipelines, with 2024 EBITDA margins at 9.8%, utilization at 68.9%, and revenue growth at 4.6%. The post attributes this to fragmented finance workflows spanning Salesforce, SAP, Workday, Concur, and spreadsheets, plus inconsistent metrics and manual reconciliation. It proposes using Databricks to create a governed data layer with Lakeflow, Lakehouse, and Unity Catalog, then adding Genie for natural-language analysis and finance apps that trigger workflows and write approvals back in real time. A global consulting firm reportedly cut cash forecasting cycles by 3–5 days and reduced reporting-cycle FTE hours by 80%, while Databricks’ own close fell from 15 days to 8. The proposed rollout spans 10–12 weeks and begins with one tightly scoped workflow such as revenue close, unbilled WIP, T&E leakage, or practice-margin analysis.
An Engineer’s Guide to Better AI Skills: Implementing a Testing Process to Optimize Agent…
Software engineers deploying AI agents often encounter unreliable skill invocation for repository-specific architectures, such as Pinterest's iOS framework. To quantify and resolve this issue, a Bash test harness piped positive and negative prompts into Pin-agent and Claude Code while parsing streamed JSON logs for execution signatures. Initial vanilla benchmarks yielded baseline accuracies of 73% for GPT 5.2-codex and 62% for Opus 4.5 Claude. Optimizations such as expanding YAML frontmatter descriptions with architectural context, utilizing capitalized imperative directives, and adding reference tables in AGENTS.md raised invocation rates. Both agents reliably loaded skills when prompts contained explicit invocations, demonstrating that empirical testing paired with descriptive prompting stabilizes agent skill adoption.
How we achieved truly serverless GPUs
GPU inference workloads suffer from spiky demand, leading to low GPU Allocation Utilization when capacity is fixed. Modal reduced replica startup latency from over 2 kiloseconds to about 50 seconds using four techniques. A buffer of idle GPUs removes instance allocation from the hot path. A custom content-addressed filesystem serves container images lazily from a multi-tier cache. CPU and CUDA checkpoint/restore fast-forward through initialization by directly restoring processes and GPU contexts into memory. These optimizations enable truly serverless GPU inference, as demonstrated by Reducto’s document processing scaling to thousands of GPUs with cold starts reduced from ~70s to ~12s.
We replaced Redis with MySQL for inventory reservations—and it scaled
Shopify replaced its Redis-based inventory reservation system with MySQL to unify reservations with the inventory ledger and provide ACID coordination between payment and inventory updates. The new design stores one row per sellable unit in a bounded pool of up to 1,000 rows per item/location combination, uses MySQL 8's SKIP LOCKED to avoid waiting on locked units, and replenishes depleted pools. Composite primary keys reduced row locks, READ COMMITTED avoided gap locks that interfered with replenishment, and consistent lock ordering prevented deadlocks. The investigation found connection-pool usage elsewhere in checkout, rather than reservation query speed alone, was the immediate ceiling; cleanup removed 50% of primary-database reads and 33% of transactions, while configuration changes added headroom. Shopify validated the design in shadow mode, then rolled it out gradually with a Redis rollback path, meeting high-throughput targets during peak 2025 traffic.
Unlocking the Archives: Turning Unstructured Documents into a Searchable Database for Groundwater Discovery
MapAid and the Sudan Association for Archiving Knowledge (SUDAAK) needed to make nearly 700 scanned geological reports searchable and recover well data for MapAid’s WellMapr groundwater models. Databricks built a Databricks-only pipeline that renders pages as images, uses multimodal AI Functions for page classification and schema-constrained JSON, and applies targeted sampling to cut classification processing by more than 70%. Water-relevant documents receive page-by-page OCR through the Foundation Model API, with well and borehole identifiers linking information across pages before structured records capture locations, depths, static water levels, and yields. In its first full run, 654 documents and 5,570 pages were classified in under three hours; 95% received excellent or good evaluations, about half contained water data, and 299 well or borehole records were extracted.
Fighting Tool Sprawl: The Case for AI Tool Registries
Rapid enterprise AI agent adoption without centralized infrastructure introduces widespread tool sprawl, duplicated engineering effort, and severe operational opacity. Individual application teams frequently build ad hoc, undocumented integrations, leaving security teams unable to discover, audit, or approve active agent capabilities. Internal enterprise tool registries address this coordination failure by establishing an organization-specific catalog tailored to internal security policies, data, and compliance requirements. A production-grade registry delivers core discovery and version tracking functionality while surfacing security certification metadata and enabling consistent identity-scoped access control across teams. Organizations establishing centralized tool infrastructure can accelerate agent onboarding, implement deny-by-default governance policies, and mitigate technical debt before uncoordinated deployments compound across the enterprise.
Retail markdown optimization: from reactive markdowns to proactive
Retail markdown optimization addresses the gap between changing demand signals and delayed merchandising action. The post defines it as strategically reducing prices on slow-moving or end-of-life inventory, using demand forecasts, sell-through rates, weeks of supply, and price-elasticity models to choose which SKUs to mark down, when to start, how deep to discount, and where to act. It presents Databricks Genie as a natural-language interface across e-commerce, store, and wholesale data, with supplier, margin, and historical-pattern context; one example is Coop’s Microsoft Teams assistant, AskCap, which reported a 30% retention rate among internal users. Earlier detection of sell-through deceleration can give merchants more time to protect margin, adjust open-to-buy, and reallocate capital, while Genie supports decisions rather than making them.
From vibe code to production-ready: observability for Next.js and Supabase apps
AI-assisted software development often generates functional code that lacks proper instrumentation, leading to unindexed database queries, N+1 patterns, and fragmented connection strategies. While Supabase provides built-in query analytics, security advisors, and subsystem logs, it cannot trace distributed requests across the entire application stack. Integrating Sentry bridges this visibility gap by establishing distributed traces across Next.js frontends, Deno-based Supabase Edge Functions, and Postgres databases. Teams can forward Supabase logs into dedicated Sentry projects and configure runtime integrations to automatically detect performance bottlenecks such as slow spans and Web Vitals regressions. Furthermore, integrating Model Context Protocol servers allows Sentry's Seer debugger to identify root causes and generate automated fixes for emerging issues.
Powering self-driving vehicle analytics at Avride with ClickHouse Cloud
Avride’s expanding autonomous passenger vehicle and delivery robot fleet generated thousands of data points per minute, overwhelming its legacy Apache Iceberg and Parquet metadata indexing architecture. Iceberg’s optimistic concurrency model struggled with parallel writes, pushing ingestion latencies to hours or days, inflating query times to 10–20 seconds, and requiring raw data duplication across storage locations. To resolve these bottlenecks, Avride built a custom indexing and metrics warehouse powered by ClickHouse Cloud on AWS backed by Amazon S3 object storage. The new architecture dropped ingestion delays to seconds, reduced worst-case lookup latencies to under one second, eliminated the need for bespoke caching layers, and removed payload duplication. Beyond operational analytics, the unified ClickHouse platform now supports internal ride-replay debugging tools, simulation workflows, and high-cardinality vehicle C++ performance trace profiling.
Upgrading M3DB from v1.1 to v1.5
Uber upgraded M3DB from v1.1 to v1.5 after three years on its previous open-source deployment, seeking a repeatable rollout process for a roughly 100-cluster, 5,500-node environment. The effort addressed scale-specific validation: offline benchmarks measured correctness, latency, success rates, bootstrap and tick durations, and persistence across restarts and replacements, while online shadow testing compared sampled production traffic between old and new versions. Within each cluster, upgrades were limited to one node at a time because writes used a two-thirds majority quorum. A rollout-related read-quorum incident exposed missing replica data; the cause was bootstrap misconfiguration, corrected by using “exclude_commitlog,” since production workloads did not employ WAL. Production then showed significantly lower memory usage and improved node bootstrap times, supporting continued, data-driven upgrades and contributions to M3DB's benchmarking and documentation.
How Superhuman and Databricks built a 200K QPS inference platform together
Superhuman replaced a DIY vLLM-based serving stack for its grammar-correction model with Databricks model serving, targeting peak traffic above 200,000 QPS, sub-second P99 latency, four-nines reliability, and no quality regression. The platform combines an Endpoint Discovery Service with power-of-two-choices load balancing, concurrency-based autoscaling, and asymmetric scale policies to reduce hotspots and handle rapid traffic ramps. Lazy-loading container images changed startup from several minutes to a few seconds by fetching metadata first and retrieving 4MB blocks on demand. Runtime work on H100 GPUs raised per-pod throughput from 750 to 1,200 QPS: FP8 weight quantization contributed up to 30%, while a multiprocessing server added 20%, alongside C++ and asynchronous scheduling optimizations. The teams validated changes through shadow testing and evaluation harnesses, and Superhuman is migrating additional models and workflows to the platform.
Enhancing Ad Relevance: Integrating Real-Time Context into Sequential Recommender Models
Pinterest's sequential ads candidate generator inferred user embeddings offline from historical offsite conversions, which lacked real-time browsing context. Consequently, less than 1% of impressions on the Related Pins surface were attributed to this generator because its retrieved candidates struggled in downstream ranking. To resolve this limitation, engineers developed the Contextual Sequential Two Tower Model, integrating a context layer directly into the query tower alongside user demographic features. Offline training utilized synthetic context derived from positive conversion labels paired with high dropout rates to preserve sequence reliance, while serving split into offline Transformer inference and online context computation. Offline evaluations showed a 3x to 10x increase in Recall@K, and production deployment lifted median candidate relevance by 275% to 300% and overall ROAS by approximately 0.7%.
Addressing HR's widening capacity gap with AI
HR teams face a widening capacity gap as strategic expectations, complex employee issues, workforce volatility, skills shortages, and demands for personalized support collide with largely unchanged headcount and tools. The article presents AI transformation as an incremental journey: first establish a secure Employee 360 from structured and unstructured enterprise data, then build reusable workforce insights, augment workflows with human oversight, and progress toward broader transformation. It emphasizes data governance, including access controls, auditing, quality, standardization, and reliable interpretations, while noting that trust has limited AI’s business impact so far. MathCo and Databricks support this roadmap through NucliOS, whose Data Studio, AI Studio, and Decision Studio environments connect governed data, explainable models, feedback loops, and decision applications; Databricks supplies the lakehouse foundation, lineage, quality checks, and privacy-compliant access.
How we turned support into a revenue engine at Intercom
After Fin improved resolution rates and efficiency at Intercom, support gained capacity and began testing a consultative role focused on customer value and company goals. The team generated campaign ideas through a support town hall, then assigned a small volunteer group to proactively help self-serve customers with Fin trials, optimization, and adoption. Volunteers were trained to probe customer context rather than only resolve immediate issues, while sales and success teams provided guidance on timing and outreach. Over six months, engaged accounts grew roughly twice as fast in usage and expansion as accounts contacted without a response; the team also tracked resolutions, automation, and influenced expansion ARR. The initiative expanded into cross-functional partnerships and a ~16-person volunteer group, positioning support as a contributor to adoption, retention, and expansion revenue.
Energy trading analytics in a real-time market
Energy trading markets settle every 15 minutes, while many analytics environments still rely on nightly batch pipelines, creating latency between changing conditions and trading decisions. The post presents Databricks Genie as a conversational interface that lets traders and portfolio managers query actual market data, weather integrations, historical position data, and current books. Its example combines a node’s 90-day natural-gas basis distribution with a Northeast temperature anomaly, while the described system can query real-time and historical data together. Genie also includes settlements, curtailments, and ancillary-service data, with governed access so users see only authorized information. The conclusion is that faster data access can remove information latency and compound margin without replacing trading expertise; the source states that Genie is available today.
First-party audience data is the ad sales relationship now
The post argues that media companies face a stronger need to turn first-party audience data into an advertising sales advantage as third-party cookies decline and buyer attribution expectations rise. Advertisers are buying audiences rather than inventory, so sales teams need granular audience insights, validated campaign performance, clear attribution, and revenue context to compete in RFPs. Databricks Genie is presented as a natural-language interface to governed first-party data, allowing leaders to query defined audience segments such as users who completed at least 75% of automotive content in 90 days, segmented by income quartile and household ownership. The same analytical environment connects demographic composition, content and behavioral signals, post-campaign measurement, attribution, and revenue pacing against budget and prior year.
Why telecom churn prediction misses the intervention window
Telecom churn programs often intervene after customers have already shifted behavior, contacted support, or decided to leave, even though earlier signals exist in operational data. The post frames the gap as organizational: propensity models may be sophisticated, but retention leaders need timely, specific answers about high-value customers, likely triggers, and historically effective interventions. Databricks Genie is presented as a natural-language interface over customer behavioral and commercial data, able to surface targets such as premium postpaid customers with usage declines above 20%, recent support contacts, and contracts ending within 90 days. Its described capabilities combine usage, support, billing, network experience, competitive tenure, and intervention history while supporting segment and individual analysis. The proposed operating model prioritizes interventions by customer lifetime value and aims to act early enough for retention efforts to change outcomes.
Public health intelligence shouldn't require a data scientist
State, tribal, local, and territorial (STLT) health agencies manage data across surveillance, vital records, Medicaid, WIC, and emergency preparedness systems, but those systems are fragmented and difficult to query together. That limits rapid public health intelligence: questions linking emergency-department visits with pharmacy dispensing, school absenteeism, vaccination, demographic, or geographic data can require epidemiologists to assemble manual queries over weeks, even when decisions require answers within hours. The post presents Databricks Genie as a natural-language interface for querying this environment, backed by a Databricks engine that handles petabyte-scale datasets across real-time streams and historical records. It describes cross-program synthesis, Unity Catalog row- and column-level access controls, HIPAA-compliant governance, traceability to the underlying query, and validation controlled by health experts. Examples include county-level influenza-like illness trends overlaid with vaccination coverage and identifying counties with high opioid overdose rates and low treatment utilization; Genie is described as available today.
Mean time to detect is a data access problem
Security operations centers measure MTTD, MTTR, false-positive rates, and analyst utilization, yet investigations often stall because analysts assemble evidence across fragmented systems. A single alert may require separate queries for logs, identity records, asset information, prior alerts, and cross-source timelines, making the analyst the integration layer and creating an MTTI bottleneck. The post presents Lakewatch with Databricks Genie as an agentic interface powered by Anthropic Claude models: analysts ask natural-language questions while autonomous agents hunt, summarize, correlate, and reconstruct timelines across security, IT, and business data. It argues that this architecture can reduce investigation work from manual, multi-system workflows to answers in seconds, while retaining analyst-level access controls and governed data access as exploit time has shrunk to 1.3 days.
Enhancing Flink deployment with shadow testing
Grab’s Coban data streaming team addresses production-only Apache Flink deployment failures, which can leave an application unavailable for about 10 minutes while the system observes it and rolls it back. Its Shadow Testing stage runs a new version in parallel with the current Main version, replaying production data without affecting Main outputs and allowing behavior and regressions to be assessed before promotion. The Shadow deployment uses a shadow Kubernetes namespace, a shadow-prefixed application name, isShadow=true, a distinct Job ID, isolated sinks, and connector-specific separation such as suffixed Kafka consumer groups and shifted Debezium Server ID ranges. By default, Shadow runs for one hour while its job-manager status is monitored; users then continue to Main deployment, which receives a 10-minute health check and automatic rollback if unhealthy. The framework isolates Kafka and S3 outputs, prefixes metrics and logs for observability, and plans more connector support.
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