- Home
- Services: Business Analytics & Streaming
Business analytics solutions built to run in real-time
We architect and deploy data & analytics solutions for real-time environments, applying streaming where it creates a genuine advantage, so decisions can be made while opportunities still exist.
Real-time data challenges we help you solve
The right architecture for the right decision
Some decisions genuinely need real-time data, while others don't. We help identify where streaming earns its complexity and where it doesn't.
Analytics that surface problems as they emerge
Event data gets analyzed as it arrives, giving operating teams a chance to respond before a small issue becomes a larger one.
Real-time capability added to what already works
Existing data & analytics infrastructure can be extended to handle streaming use cases, often without a full rebuild of what's already in place.
Architecture that holds up as activity increases
Streaming architecture gets designed to handle rising transaction and event volume, so performance holds steady as data requirements grow.
How we approach real-time analytics and streaming
Streaming where it's warranted, not by default
Every engagement starts by evaluating whether a use case genuinely benefits from real-time processing before recommending the added complexity it requires.
Deep experience across the data stack
Real-time capability is layered onto the same data engineering and analytics expertise we apply to every project, not treated as a separate discipline.
Reliable under real operating conditions
Streaming pipelines are built to handle out-of-order events and temporary outages, so real-time doesn't come at the cost of accuracy.
Built to extend, not just launch
Analytics architectures are designed with future event volume and new use cases in mind, avoiding unnecessary rebuilds as needs evolve.
The practices behind the outcomes
Real-Time Architecture Assessment
Existing systems and use cases get evaluated to determine where streaming adds genuine value.
Streaming Data Pipeline Design
Data pipelines are architected to process and move data continuously as it's generated.
Event-Driven Analytics
Business events are analyzed as they occur, connecting activity to immediate, actionable insight.
Anomaly & Fraud Detection
Unusual patterns are flagged in real time, catching issues before they escalate.
Operational & Monitoring Analytics
Live system and performance data are surfaced directly to the teams responsible for it.
Hybrid Batch & Streaming Integration
Real-time streams and historical batch data get combined into a single, coherent analytics layer.
Related Services
Explore these additional services from our Data & Analytics capability pillar.
We design unified data platforms that break down silos, making information accessible across your organization.
We build fully integrated systems of intelligence enabling you to monitor performance, predict patterns, and automate analysis.
We implement governance frameworks that ensure compliance, quality, and trust.
Built to pivot without losing velocity
Full product teams
Forward-deployed engineering
Co-sourced delivery
Rapid capacity deployment
Team augmentation
Business analytics consultants who have been there, done that
No engagement is one-size-fits-all. Our data experts adapt to your stage, your stack, and your team.
Practice Lead, AI & BI
Over 20 years leading complex data programs for global enterprises.
Data & Analytics Lead
Over 20 years of Business Intelligence & Data Warehouse expertise.
Business Intelligence Engineer
Global expertise in building scale-ready data systems across diverse tech stacks.
Case Studies
Fleet battery intelligence customer portal and installation app
Our approach
With little formal documentation to lean on, Coherent’s team relied on frequent, direct conversations with the client’s technical staff to confirm new features as they completed each step of the project:
- Codebase archaeology and consolidation: engineers worked through the four mismatched mobile repositories, cross-referenced them against the live app store build, and reconstructed the most current working codebase before making any changes.
- Environment separation: introduced dedicated development, QA, and production environments in place of the single, undifferentiated setup the client had been running.
- Cloud infrastructure cleanup: rebuilt cloud build pipelines and restructured infrastructure, improving reliability, while bringing monthly cloud costs down from roughly $8,000 to about $1,500, an 81% reduction.
- New device API: built a new backend API, on a Grails and Java/Spring stack, that takes raw measurements from the client's new refraction-testing hardware, applies the client's formula, and returns validated results ready for review by a licensed doctor.
The solution
Today, the platform supports a complete at-home vision-testing workflow. Users complete a guided assessment with the client's hardware, results are processed through Coherent-built backend logic, and outcomes are routed to a licensed eye care professional for a prescription. The underlying infrastructure now runs across separate dev, QA, and production environments at a fraction of its previous cloud cost, giving the client a stable foundation to build on as it brings new devices to market.
The impact
- 81% reduction in monthly cloud costs, from about $8,000 to about $1,500
- New device API delivered in about 9 months, supporting the client's newest hardware line
- New development, QA, and production environments to replace the client’s single setup, consolidating four mismatched mobile repositories into one working codebase
The challenge
An undocumented, fragmented codebase
Before building the API or writing new code, Coherent needed to identify the code currently driving the client’s companion app. With no product requirements documentation and four mismatched repositories to sort through, they had to cross-reference what was on old developer laptops against the live versions in the app stores.
No environment separation and rising cloud costs
The client’s systems and software were running in a single environment — without a clear separation between development, QA, and production — which increased the likelihood of issues like cascading errors. Additionally, cloud infrastructure spend was disproportionate to the size of the product, running close to $8,000 per month.
New hardware, new math
The client was bringing a new device to market that paired with the user’s smartphone and measured refractive error using patented optical technology. Coherent needed to build the backend logic that would take the client's proprietary formula, apply it to raw device measurements, and return an accurate, prescription-ready result.
, hide_section_=true, module_id=223640941568}, css={}, child_css={}, id=module_179121390029660, name=module_179121390029660, type=module, order=5, label=Case Study Content Section 1, smart_type=null, styles={}, module_id=223640941568}, module_17923271166927={body={hide_show_module_=false, module_id=224756528181}, css={}, child_css={}, id=module_17923271166927, name=module_17923271166927, type=module, order=10, label=Case Study Video Banner Block, smart_type=null, styles={}, module_id=224756528181}, module_179120410606221={body={counter=[], logo_image={src=}, module_id=223627951216}, css={}, child_css={}, id=module_179120410606221, name=module_179120410606221, type=module, order=2, label=Case Study Posts Title, smart_type=null, styles={}, module_id=223627951216}, module_179121475366271={body={discription=What's next
Through AKTIV, Coherent’s dedicated fitness and wellness practice, the company will continue to support the client’s ongoing platform enhancements across mobile, backend, DevOps, QA, and project management. Using the same consumer healthtech context that shaped the client’s rebuild, AKTIV's engineers will also ensure the client’s hardware, app, and backend continue to evolve together as the device line matures.
, hide_section_=true, module_id=223641064784}, css={}, child_css={}, id=module_179121475366271, name=module_179121475366271, type=module, order=11, label=case-study-content-section-4, smart_type=null, styles={}, module_id=223641064784}, module_179015393630755={body={heading_group=More Case Studies
, hide_author_name_=false, module_id=224238135902, select_blog=224579243279, select_tag=healthtech-life-sciences}, css={}, child_css={}, id=module_179015393630755, name=module_179015393630755, type=module, order=12, label=Related Case Study Posts, smart_type=null, styles={}, module_id=224238135902}, module_179121160763349={body={module_id=223636303144, slider_image=[]}, css={}, child_css={}, id=module_179121160763349, name=module_179121160763349, type=module, order=8, label=fullwidth-image-slider, smart_type=null, styles={}, module_id=223636303144}}Our approach
As the client's partner, Coherent Solutions engineered an NLP solution that analyzes online product reviews. Aspect-based sentiment analysis makes it possible to see which product characteristics customers react to, and how, across a large set of reviews.
Cloud engineering and scalable data processing allowed the solution to handle the large volume.
, hide_section_=true, module_id=223641064615}, css={}, child_css={}, id=module_179121423312165, name=module_179121423312165, type=module, order=7, label=Case Study Content Section 2, smart_type=null, styles={}, module_id=223641064615}, module_179121449478468={body={discription=The solution
Coherent Solutions contributed:
- AI and machine learning engineering for a natural language processing solution built on aspect-based sentiment analysis
- Data processing designed for large volumes of customer feedback
- Cloud engineering supporting scalable data processing
The impact
The engagement produced the following results:
- Sentiment identified for specific product characteristics
- Product strengths and weaknesses identified from customer feedback
- Customer feedback processed at scale
The challenge
Reviews often mention several characteristics of a product in the same text, each with its own opinion. An overall review score or sentiment label cannot capture that, and the client needed to know what customers thought about individual characteristics of each product.
The work covered hundreds of thousands of online product reviews, so the solution had to process customer feedback at scale.
, hide_section_=true, module_id=223640941568}, css={}, child_css={}, id=module_179121390029660, name=module_179121390029660, type=module, order=5, label=Case Study Content Section 1, smart_type=null, styles={}, module_id=223640941568}, module_17923271166927={body={hide_show_module_=false, module_id=224756528181}, css={}, child_css={}, id=module_17923271166927, name=module_17923271166927, type=module, order=10, label=Case Study Video Banner Block, smart_type=null, styles={}, module_id=224756528181}, module_179120410606221={body={counter=[], logo_image={src=}, module_id=223627951216}, css={}, child_css={}, id=module_179120410606221, name=module_179120410606221, type=module, order=2, label=Case Study Posts Title, smart_type=null, styles={}, module_id=223627951216}, module_179121475366271={body={discription=What's next
How we worked here is how we work on every engagement. Whatever form your data takes, we start with the questions you need answered, select the right approach, and engineer the data processing and cloud deployment so the answers are ready to be used.
, hide_section_=true, module_id=223641064784}, css={}, child_css={}, id=module_179121475366271, name=module_179121475366271, type=module, order=11, label=case-study-content-section-4, smart_type=null, styles={}, module_id=223641064784}, module_179015393630755={body={heading_group=More Case Studies
, hide_author_name_=false, module_id=224238135902, select_blog=224579243279, select_tag=technology-saas}, css={}, child_css={}, id=module_179015393630755, name=module_179015393630755, type=module, order=12, label=Related Case Study Posts, smart_type=null, styles={}, module_id=224238135902}, module_179121160763349={body={module_id=223636303144, slider_image=[]}, css={}, child_css={}, id=module_179121160763349, name=module_179121160763349, type=module, order=8, label=fullwidth-image-slider, smart_type=null, styles={}, module_id=223636303144}}Our approach
We started working on two products. The early effort went into the MVP to establish the core architecture of the customer portal, a web application for fleet managers. Our team delivered all scheduled work on time for the release, and the MVP successfully went live.
In parallel, our team carried out the analysis for the installation app, a mobile application for technicians who install batteries and related equipment on trucks and trailers. The goal of this phase was to agree on the scope for the app’s own MVP. It resulted in an approved clickable prototype of the main flow, a solution design, an approved baseline scope document, a test plan and test strategy, an estimation, and a timeline. The app’s MVP reached the App Store and Google Play.
, hide_section_=true, module_id=223641064615}, css={}, child_css={}, id=module_179121423312165, name=module_179121423312165, type=module, order=7, label=Case Study Content Section 2, smart_type=null, styles={}, module_id=223641064615}, module_179121449478468={body={discription=The solution
The customer portal grew over eleven releases, with each stage providing value to fleet managers.
MVP. A landing page with fleet-level widgets for idling hours, fuel savings and CO2 reduction, and an idling dashboard. The data comes from the manufacturer’s data platform, and sign-in is secured through single sign-on.
Fleet health. Battery status per vehicle, filtered by vehicle type and status. Detail pages cover driver response, fuel savings and CO2 reduction, and data can be exported for offline work.
Administration. Tools to manage users, clients and locations, customer configuration, a vehicle history card, and control over which data each user can see.
Alerts and reporting. Immediate actions, notifications and reporting.
The architecture was set in the MVP and will support future battery management capabilities. The same foundation will support future battery management capabilities.
The installation app gives technicians a guided workflow on a phone, and each line below shows what installers can do with it.
Guided installation.A step-by-step flow with scanning, photo confirmation and offline synchronization, available on the App Store and Google Play.
Battery tracking.Records for truck and trailer batteries and installed equipment, plus a battery swap flow that keeps the battery’s history intact.
Web version. Installation management from a browser, with region-specific battery configurations for North America and EMEA, and German localization.
The impact
Our client realized digital value across both products, with these results:
- Increased visibility into fleet battery health and faster identification of battery issues
- Enhanced fleet monitoring and reporting
- Better administrator control over portal configuration
- Improved customer self-service and reduced manual operational effort
- Eliminated manual and paper-based installation processes, improving consistency and data quality
- Increased installation accuracy through automated validation, QR and barcode scanning, and structured data capture
- Improved traceability of batteries, installed equipment, and vehicle assets throughout their lifecycle
- Expanded operational visibility through centralized reporting, installation tracking, and enterprise data management
The portal now scales to enterprise fleets. From the first release, the work carried a clear business impact, and this approach to closing the gap between technology investment and business outcomes is what we call Digital Value Creation.
, hide_section_=true, module_id=223640945533}, css={}, child_css={}, id=module_179121449478468, name=module_179121449478468, type=module, order=9, label=Case Study Content Section 3, smart_type=null, styles={}, module_id=223640945533}, module_179121507763678={body={discription=[11 production releases — since September 2025, 2 products — a customer portal and an installation app, On-time MVP delivery — all scheduled work delivered for the first release], module_id=223640945724}, css={}, child_css={}, id=module_179121507763678, name=module_179121507763678, type=module, order=6, label=case-study-three-column-card, smart_type=null, styles={}, module_id=223640945724}, module_17921485850025={body={module_id=224578905090, quotes_description=, quotes_image={alt=, src=}}, css={}, child_css={}, id=module_17921485850025, name=module_17921485850025, type=module, order=4, label=case-study-full-width-quotes-block, smart_type=null, styles={}, module_id=224578905090}, module_179121390029660={body={discription=The challenge
The manufacturer wanted to give its fleet customers direct access to the data its batteries produce. That meant a secure and scalable portal, built on a foundation that could take new features independently, so that fleet managers could see insights about their own fleets.
The second problem was in the field. Technicians installed batteries and related equipment on trucks and trailers by hand, using paper forms, so installation records were inconsistent and hard to trace.
, hide_section_=true, module_id=223640941568}, css={}, child_css={}, id=module_179121390029660, name=module_179121390029660, type=module, order=5, label=Case Study Content Section 1, smart_type=null, styles={}, module_id=223640941568}, module_17923271166927={body={hide_show_module_=false, module_id=224756528181}, css={}, child_css={}, id=module_17923271166927, name=module_17923271166927, type=module, order=10, label=Case Study Video Banner Block, smart_type=null, styles={}, module_id=224756528181}, module_179120410606221={body={counter=[], logo_image={src=}, module_id=223627951216}, css={}, child_css={}, id=module_179120410606221, name=module_179120410606221, type=module, order=2, label=Case Study Posts Title, smart_type=null, styles={}, module_id=223627951216}, module_179121475366271={body={discription=What’s next
Both products continue to evolve. The customer portal roadmap includes additional analytics, parasitic draw, jumpstart tracking, and custom export. Its architecture is ready for future battery management capabilities.
The current priorities for the installation app include improving data quality through additional information about assets, tighter integration with the manufacturer’s backend systems, and new workflows for flexible battery configurations and equipment swaps. These workflows will support field services and supply chain capabilities.
, hide_section_=true, module_id=223641064784}, css={}, child_css={}, id=module_179121475366271, name=module_179121475366271, type=module, order=11, label=case-study-content-section-4, smart_type=null, styles={}, module_id=223641064784}, module_179015393630755={body={heading_group=More Case Studies
, hide_author_name_=false, module_id=224238135902, select_blog=224579243279, select_tag=manufacturing-industrial-services}, css={}, child_css={}, id=module_179015393630755, name=module_179015393630755, type=module, order=12, label=Related Case Study Posts, smart_type=null, styles={}, module_id=224238135902}, module_179121160763349={body={module_id=223636303144, slider_image=[]}, css={}, child_css={}, id=module_179121160763349, name=module_179121160763349, type=module, order=8, label=fullwidth-image-slider, smart_type=null, styles={}, module_id=223636303144}}Ready to explore real-time analytics and streaming for your environment?
Relevant
Questions
The deciding factor is whether a delayed decision creates real cost or risk, like fraud detection or equipment failure. If a daily or hourly refresh wouldn't change the outcome, batch processing is usually simpler and cheaper to maintain.
Streaming systems require continuous compute resources and more complex infrastructure to manage state and fault tolerance, which raises both build and operating costs. Batch processing runs on a schedule and can scale compute up only when needed, making it considerably less expensive for use cases that don't require immediacy.
Redundancy at the message broker level, combined with dead-letter queues for failed events and clear fallback behavior, prevents a pipeline outage from silently dropping data. Testing failure scenarios before launch matters more here than in batch systems, since there's no next scheduled run to catch up.
Change data capture tools can feed warehouse tables incrementally as source data changes, avoiding the need to re-architect the entire warehouse. This approach works well when the goal is fresher data rather than sub-second processing.
Bottlenecks usually stem from under-partitioned topics, processing logic that can't scale horizontally, or downstream systems that can't absorb the write throughput. Diagnosing which stage is the actual constraint matters more than simply adding more compute across the board.
Consumer lag, end-to-end latency, and error rates matter more than uptime alone, since a pipeline can be technically running while falling further behind in real time. Alerting on lag trends catches degradation before it becomes a visible outage.