Service Details
Database & Reporting Performance
Finance closes, manufacturing dashboards, and operational reports all depend on predictable data platforms. When queries stretch into minutes and nightly jobs creep into business hours, the rest of the company feels it.
When it’s a fit
- Business-critical reports or dashboards regularly exceed acceptable runtimes or time out entirely.
- Batch processing windows (invoicing, reconciliation, ETL) overrun into the next workday.
- Database growth outpaces retention and backup plans, threatening both storage and performance.
- Frequent blocking or deadlocks require constant operator intervention during peak hours.
How we work
- Capture real workload characteristics (query stats, plan cache, traces) across SQL Server, PostgreSQL, MySQL, or InterSystems Caché.
- Audit schemas, indexing strategies, and ORM usage to remove unnecessary scans and round-trips.
- Separate operational and analytical workloads via replicas, staging layers, or export routines.
- Introduce lightweight observability—query baselines, job runtimes, storage growth tracking—so regressions are visible early.
Typical deliverables
- A concise performance brief summarising the top bottlenecks with before/after measurements.
- A prioritized backlog covering the most impactful schema/index updates, query rewrites, or scheduling changes.
- Guidance on retention/archival strategy to keep growth under control without losing auditability.
- Simple instrumentation scripts or dashboards that plug into your existing monitoring.
Engagements often run as a one-week assessment followed by a focused implementation sprint.
Legacy Core Stabilisation & Refactoring
ERP customisations, inventory synchronisation, or bespoke billing engines often accumulate a decade of quick fixes. When a single module controls revenue recognition or production scheduling, touching it without a plan feels reckless.
When it’s a fit
- The codebase mixes VB6/C#/C++/Caché layers with little documentation and unclear ownership.
- Every change triggers regression firefights or requires code freezes because dependencies are unknown.
- Only one or two senior engineers remember the intent, and knowledge transfer is at risk.
- Auditors or customers demand stability guarantees the current system cannot provide.
How we work
- Perform “code archaeology”: trace execution paths, data mutations, and runtime configuration.
- Draw dependency and data-flow maps to expose coupling, shared state, and integration seams.
- Design safe seams (facades, adapters, anti-corruption layers) so high-risk logic can be isolated gradually.
- Add guardrail tests (database fixtures, integration harnesses, golden files) before modifying behaviour.
- Pair with your team to implement the first refactoring slices and transfer the new structure.
Typical deliverables
- A module map highlighting responsibilities, dependencies, and the riskiest seams.
- A sequenced stabilisation plan describing recommended refactoring slices and safeguards.
- Guardrail tests or diagnostic tools so future work can rely on repeatable checks.
- Knowledge-transfer notes or walkthroughs capturing how the module now behaves.
Most stabilisation sprints focus on one core module at a time, keeping progress controlled.
Incremental System Modernisation & Team Enablement
Architecture slides are useless if the delivery workflow keeps reinforcing the old shape. Modernisation succeeds when you pair a realistic target state with working agreements that make every sprint push in that direction.
When it’s a fit
- Modernisation has been “in progress” for years, but core capabilities are still tied to legacy systems.
- Multiple teams touch the same monolith or database with little coordination, causing regressions.
- Branches stay open for weeks, merges are painful, and nobody is confident in release readiness.
- New hires struggle to understand architecture decisions or find authoritative guidance.
How we work
- Clarify business-critical capabilities and map them to current vs. desired architecture slices.
- Sequence evolution milestones (APIs, services, data boundaries) with explicit dependencies and exit criteria.
- Align branching, code reviews, and CI/CD gates with the evolution plan so process reinforces architecture.
- Facilitate architecture clinics, threat-model sessions, or code reviews using your actual repositories.
- Create simple working agreements (definition of done, PR templates, release checklists) the team can self-enforce.
Typical deliverables
- A modernisation roadmap outlining current vs. target capabilities, dependencies, and risk mitigations.
- A 30/60/90-day execution outline with accountable owners and measurable checkpoints.
- Refreshed engineering playbooks (branching, review, release) aligned to the evolution plan.
- Workshop notes or recordings that capture the architecture decisions for onboarding and audits.
The goal is a modernisation effort that moves in small, measurable steps, reinforced by daily practice.
AI-Assisted Workflows
When AI stays focused on concrete bottlenecks—log triage, document summarisation, developer hand-offs—it becomes an efficiency tool rather than a science experiment.
When it’s a fit
- Engineers waste hours rewriting similar responses, changelog entries, or boilerplate code.
- Ops teams sift through long log files or incident timelines manually before escalating.
- Institutional knowledge lives in wikis or PDFs that nobody can search effectively.
- The organisation wants to explore AI responsibly but lacks internal guardrails or prototypes.
How we work
- Interview stakeholders to pinpoint repetitive, measurable tasks suited for assistance.
- Assess data readiness, privacy concerns, and integration points with existing systems.
- Prototype assistants or automations (CLI helpers, chat integrations, scripts) using trusted APIs such as OpenAI or local models.
- Define prompt patterns, guardrails, logging, and rollback plans so adoption stays safe.
- Measure impact (time saved, incidents avoided) and outline the path from pilot to production.
Typical deliverables
- An AI use-case brief outlining objectives, constraints, and required data safeguards.
- A working pilot (script, bot, or service) with deployment notes and prompt guidance.
- Operational guidelines detailing guardrails, fallback behaviour, and ownership.
- A lightweight adoption plan with success metrics to decide whether to scale or park the idea.
Each engagement stays short: identify the workflow, build a pilot, measure, and either deploy or stop with clear lessons.
Ready to stabilise a critical system?
Get in touch to discuss your slow database, fragile module, or stalled modernisation and outline a focused first engagement.