OUR WORK · EXAMPLE SYSTEMS

Examples that look like your problem.

A closer look at the kinds of problems we solve, what we build, and what you get. These are representative examples, not named client engagements. Technical detail is included, but you do not need to be an engineer to follow it.

APPLICATION PLATFORM LOGISTICS & OPERATIONAL PLATFORM
EXAMPLE 01

Dispatch platform for a logistics company

The problem

A logistics company coordinated deliveries over email and shared spreadsheets. Staff retyped order details by hand, updates got missed, and nobody had a single view of what was happening.

What we built

We designed and built a custom web portal from scratch. Staff get a clear role-based interface, dispatch rules are checked automatically, and releases are automated. Delivered with full documentation and complete IP handover.

What you get

  • A web portal for staff and customers, with the right access for each role
  • Fast backend services (under 35ms response)
  • A secure database that keeps order data consistent
  • Automated releases and clear operating documentation
HOW IT WORKS IN PRODUCTION
01
Secure sign-in

Every request is authenticated, with role-based access and session checks.

02
Reliable dispatch updates

Database locks prevent duplicate or conflicting dispatch changes.

03
Automatic audit trail

Every change is recorded and tracked without slowing the application down.

18,500 Peak dispatches per hour
< 32ms Response time
0 Reconciliation Errors
16 hrs Saved per week
AI & DOCUMENT SEARCH PRIVATE DOCUMENT SEARCH
EXAMPLE 02

A private AI assistant for 4,000+ documents

The problem

A technical services firm had thousands of policies, contracts, and compliance manuals spread across different systems. Staff spent hours searching long PDFs, and public AI tools made things up or risked leaking client data.

What we built

We built a private document assistant that reads thousands of PDFs, finds the right passage, and cites the exact page before answering. It runs inside the client's own environment, and no data is used to train public models.

What you get

  • Document ingestion that keeps tables and sections intact
  • Search that always returns the source page
  • A simple API for your internal tools
  • The assistant declines when confidence is low
HOW IT WORKS PRIVATE DEPLOYMENT
01
Reads your documents

Tables, clauses, and multi-column manuals are parsed with their structure intact.

02
Finds the right passage

Meaning-based search plus exact keyword matching for rare compliance terms.

03
Cites the source

Answers include the source page, and the assistant declines when confidence is below 92%.

< 380ms Answer time
100% Answers with a source
0% Data used for training
3 mins Review Time (was 4 hrs)
DATA PLATFORMS OPERATIONS DASHBOARD
EXAMPLE 03

One clear dashboard for 48,000 events a second

The problem

Leadership had hundreds of monitoring charts and no clear answer to simple questions: is the system healthy, and what needs attention first?

What we built

We built an automated pipeline and a focused dashboard showing only the metrics leadership actually used. Queries run against read replicas, so reporting never slows down production.

What you get

  • An automated pipeline that prepares the numbers
  • A focused dashboard with the metrics leadership actually uses
  • Alerts for real problems, not noise
  • Documentation and a full handover
HOW IT WORKS LIVE DATA
01
Captures changes live

Changes flow from production systems without slowing the database.

02
Clean reporting model

A tidy reporting model with pre-computed percentiles for fast queries.

03
Dashboards & alerts

Live dashboards, schema-drift detection, and alerts before an error budget runs out.

48,000 Events / Sec Ingestion
< 18ms Query time
4.2x Data compression
< 5s Data freshness
CLOUD & DEVOPS CLOUD & RELEASES
EXAMPLE 04

Releases in 4 minutes, not 2 hours

The problem

An established SaaS product team was held back by fragile manual deployments that took over two hours per release, alongside unmonitored cloud environments that caused frequent unexpected downtime.

What we built

We automated their existing cloud setup instead of rewriting it. Releases went from two hours to four minutes, rollbacks became automatic, and right-sizing cut cloud compute costs by roughly a third.

What you get

  • Infrastructure code and environment templates
  • Automated releases with staging checks
  • Zero-downtime deployment setup
  • Uptime monitoring and automatic alerts
INFRASTRUCTURE TELEMETRY
PIPELINE VELOCITY 99.98% UPTIME
Deploy time: 4m 12s (was 2h 10m)
Monthly cloud bill: 32% lower
Rollback: Under 30 seconds, automatic
TECHNICAL AUDIT TECHNICAL AUDIT
EXAMPLE 05

A rewrite avoided, and an AI plan that made sense

The problem

A growing company was about to spend millions on an application rewrite and an AI initiative. Leadership wanted an independent, vendor-neutral review before committing.

What we built

We reviewed the database, APIs, and security posture over two weeks. The honest finding: 80% of the gains could come from better indexing and query patterns, not a multi-million dollar rewrite. We also delivered a targeted AI feasibility plan.

What you get

  • Comprehensive architecture audit report and risk matrix
  • AI feasibility & ROI analysis (clear build-vs-buy guidance)
  • Prioritised 3-stage optimisation roadmap for in-house engineering
  • Executive board presentation with verified benchmark data
ADVISORY OUTCOME REPORT
DIAGNOSTIC FINDING HIGHLIGHT

“Identified six figures in avoidable rewrite spend by resolving four core database index locks and refactoring batch sync routines.”

AWS Cloud-native delivery. Diagnostic deliverables enable in-house engineering teams to execute remediation independently.
NEXT STEP

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or an idea that needs building?

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