What we
do.

Custom Software Development

Bespoke product engineering

Custom software is our oldest discipline and our default starting point. We build the systems your business runs on — from the first wireframe to the on-call rotation that keeps it healthy in year three. Our engagements typically begin with a fixed-scope discovery sprint to de-risk the build, followed by outcome-priced delivery in vertical slices. We work in cross-functional pods (product, design, engineering, QA, SRE) led by a senior architect who stays on the engagement from day one through to launch.

• 01Discovery sprint (1–3 weeks) — problem framing, architecture spike, risk register
• 02Vertical-slice delivery — working software in weeks, not quarters
• 03Trunk-based development, feature flags, continuous deployment by default
• 04Code review on every change · 80%+ test coverage on critical paths
• 05Observability designed in: structured logs, metrics, traces from day one

• →Architecture decision records (ADRs)
• →Production-grade source code under your ownership
• →Automated test suite and CI/CD pipeline
• →Runbooks and operational handover
• →30/60/90 post-launch operating support

Digital Transformation

Modernization at the system level

Most digital transformations fail not because the technology is wrong, but because the change is treated as an IT project. Hexcore approaches transformation as a business problem with technology consequences. We start by mapping value streams, identifying the two or three workflows that genuinely move your P&L, and re-platforming those first — proving the ROI before scaling the program. The result is transformation that the operating business actually adopts.

• 01Current-state audit · capability mapping · value-stream analysis
• 02Target operating model and reference architecture
• 03Phased re-platforming with parallel-run safety nets
• 04Change enablement — playbooks, training, internal capability transfer
• 05Quarterly outcome reviews tied to business KPIs, not vanity metrics

• →Capability heatmap and gap analysis
• →12/24/36-month transformation roadmap
• →Reference architecture and tech-radar
• →Pilot delivery with measured KPIs before scale-out
• →Internal capability uplift through pairing and embedded coaching

Cloud Consulting

Strategy, architecture, economics

Cloud is a financial decision as much as a technical one. We treat it that way. Our cloud consulting engagements produce a defensible cloud strategy — which workloads belong where, what the 3-year TCO actually looks like, and how to migrate without lighting the budget on fire. We're cloud-vendor-agnostic; our architects hold certifications across AWS, Azure, and GCP and have shipped production workloads on all three.

• 01Workload portfolio assessment (6 Rs framework)
• 02Total-cost-of-ownership modelling with sensitivity analysis
• 03Landing zone and account-vending design
• 04Migration wave planning with rollback safety
• 05FinOps practice setup — visibility, optimisation, governance

• →Cloud strategy document (board-ready)
• →Landing zone reference implementation
• →Migration runbook with cutover playbook
• →FinOps dashboard and savings plan
• →Cloud Centre of Excellence operating model

Cloud & DevOps Engineering

Pipelines that ship safely

World-class engineering organisations ship to production hundreds of times a day, safely. That capability is not magic — it's a stack of compounding investments in CI/CD, IaC, observability, and developer experience. We build that stack. Our DevOps engagements typically deliver measurable improvements in deployment frequency, lead time for changes, change failure rate, and mean time to recovery — the four DORA metrics — within the first six months.

• 01DORA baseline assessment · developer survey
• 02Platform engineering — internal developer platforms (IDPs)
• 03Everything-as-code: infrastructure, policy, security, runbooks
• 04Progressive delivery: feature flags, canary, blue/green
• 05SRE practice: SLOs, error budgets, blameless postmortems

• →CI/CD pipeline (multi-environment, with policy gates)
• →Terraform / Pulumi modules and reference IaC
• →Observability stack (logs, metrics, traces, alerts)
• →Incident response runbooks
• →Engineering effectiveness dashboard

Cloud-Native Development

Built for elastic scale

Cloud-native isn't a buzzword for us — it's a default operating assumption: containers, declarative infrastructure, dynamic orchestration, service mesh, and event-driven decoupling, applied with restraint. We've shipped Kubernetes platforms running 4,000+ pods across multi-region, multi-tenant clusters, and we've also told clients honestly when a managed serverless platform was the better answer. The architecture serves the business; never the other way around.

• 01Domain decomposition to identify true service boundaries
• 02Twelve-factor application principles, applied pragmatically
• 03Resilience patterns: circuit breakers, bulkheads, retries with jitter
• 04Progressive delivery and zero-downtime deployment as defaults
• 05Multi-tenancy and data isolation designed in, not bolted on

• →Production-grade Kubernetes platform or serverless reference
• →Service templates and golden paths
• →Multi-region HA / DR architecture
• →Event-driven integration patterns
• →Performance and load-test harness

Automation Testing

Quality, codified

Quality is not a phase, a team, or a dashboard. Quality is a property of the system — and you build it in by codifying it. Our testing engagements design and implement a healthy test pyramid (lots of fast unit tests, fewer integration tests, a small ring of end-to-end smoke tests), wire them into the CI pipeline as policy gates, and equip your engineers to maintain it without us. The goal is sustainable test ownership, not a bloated suite that everyone resents.

• 01Test strategy audit · current-state pyramid analysis
• 02Risk-based test prioritisation tied to business impact
• 03Contract testing to decouple services
• 04Performance / load testing baked into CI
• 05Mutation testing to verify the tests themselves

• →Test strategy document
• →Automated regression suite (unit, integration, E2E)
• →Performance harness with baselines
• →CI policy gates and quality dashboards
• →QA enablement program for your team

AI-Native Development

Products with intelligence at the core

Most teams ship a demo. We ship products. The gap between a working LLM prototype and a system you can sell to enterprise customers is mostly invisible from the outside — and it's where we live. Eval harnesses, guardrails, observability, cost controls, drift detection, prompt-versioning, retrieval quality dashboards, human-in-the-loop review pipelines. The boring infrastructure that turns an impressive demo into a durable, defensible product.

• 01Problem framing — what is this AI actually replacing, augmenting, or unlocking?
• 02Eval-first development: define success criteria before building
• 03Retrieval architecture and chunking strategy aligned to the domain
• 04Guardrails: input/output filtering, refusal handling, PII redaction
• 05Production observability: cost, latency, quality, drift

• →Production RAG / agent system under your ownership
• →Eval harness with regression suite
• →Prompt registry and versioning system
• →Cost / quality / latency dashboard
• →Human review queue for low-confidence outputs

Cybersecurity & VAPT

Defense, verified

Security work that produces a 200-page PDF and ends with a handshake is not security work. Our VAPT engagements end with verified fixes, regression tests for the vulnerabilities we found, and a hardened secure-SDLC practice so the same classes of bugs don't recur. Our offensive team is CREST-aligned and our consultants hold OSCP, OSCE, CISSP, and equivalent credentials. We work to a documented methodology you can audit.

• 01Scoping workshop · threat modelling · STRIDE / PASTA
• 02Black-box, grey-box, or white-box testing per scope
• 03Manual exploitation — not just scanner output
• 04Risk-ranked findings with remediation guidance and reproducible PoCs
• 05Retest cycle included — we verify the fixes

• →Executive summary report (board-ready)
• →Technical findings with PoCs and CVSS scoring
• →Remediation roadmap with prioritisation
• →Retest report verifying fixes
• →Secure-SDLC playbook for your engineering org

Data & AI

ML, MLOps, generative AI

Most data problems are not modelling problems — they're plumbing problems. Before we train anything, we make sure the data is reliable, observable, and contractually owned. Once the foundation is solid, we ship models with the same engineering discipline we apply to any other production system: tests, monitoring, version control, rollback. Our data & ML practice covers classical ML, deep learning, and generative AI — and we'll tell you honestly when you don't need a model at all.

• 01Data audit · lineage mapping · quality SLAs
• 02Modern data stack design: ingest, transform, serve
• 03Feature platform for reusable model inputs
• 04MLOps from day one — versioned data, models, and pipelines
• 05Continuous evaluation: drift detection, fairness, performance monitoring

• →Data platform reference architecture
• →ELT pipelines (Airflow, dbt) with tests
• →Feature store and offline/online serving
• →Model registry and CI/CD for ML
• →Production monitoring and drift detection