Enterprise Software Development Company

Large systems built for the
complexity they'll actually face.

Domain-driven. Event-sourced. Cloud-native. AI embedded as first-class components. Six verticals. One engineering team.

API Gateway
Kong · rate-limit · auth
auth-service
jwt · oauth2 · rbac
user-service
postgres · redis cache
domain-service
business logic · events
Event Bus (Kafka)
topics · partitions · consumer groups · schema registry
workflow-service
state machine · bpmn
notify-service
email · sms · webhook
✦ ai-inference-service
llm · decision engine · vector-db · rag pipeline
analytics-service
clickhouse · metrics
PostgreSQL
primary store
Redis
cache · sessions
✦ vector-db
pinecone · embeddings
Snowflake
analytics dwh
ISTIO SERVICE MESH · mTLS · CIRCUIT BREAKER · OBSERVABILITY · TRAFFIC SHAPING
KUBERNETES · HELM · ARGOCD · TERRAFORM · AWS / AZURE / GCP

Our Decade long experience, validated in numbers

50+

AI Projects Delivered

Enterprise Customers

Industries Served

95+

Customer Satisfaction

10+

Years Building AI Solutions

How agents reason

The ReAct loop the core pattern behind every agent we build.

01 Custom Platform Engineering

Domain-Driven Enterprise Platform Development

Business-critical platforms designed around your actual domain model not a generic ERP configured to approximate one. We start with event storming and domain modelling before a line of code is written, producing bounded contexts with clear ownership, independent deployment, and data autonomy. The architecture accommodates future complexity; it doesn’t just solve today’s problem.

◆ Design method

Domain-Driven Design (DDD) · event storming · context mapping.

◆ Data pattern

Database-per-service · CQRS · event sourcing where warranted.

◆ Deployment

Independent per bounded context · canary releases · feature flags.

◆ Observability

Structured logging · distributed tracing · SLO-based alerting.

02 Integration Architecture

Event-Driven Integration & API Management

Enterprise complexity lives in the integration layer. We design it as a first-class architectural concern API-first interfaces, event-driven service communication, and an explicit integration topology with documented data contracts. Not point-to-point connections that create invisible coupling and breaking changes. The integration layer is observable, versioned, and independently deployable.

◆ Ingress pattern

API gateway (Kong / AWS API GW) · rate limiting · circuit breaking

◆ Internal comms

Kafka event bus · consumer groups · exactly-once semantics

◆ Data contracts

Avro / Protobuf schemas · schema registry · consumer-driven testing

◆ Legacy bridge

Anti-corruption layer (ACL) · change data capture · adapter pattern.

03 Platform Engineering

Cloud-Native Architecture & Internal Developer Platform

Decomposed, independently deployable services on Kubernetes with the platform engineering discipline to run them reliably. Service mesh for east-west observability and mutual TLS. An internal developer platform that reduces cognitive load so product teams ship without needing infrastructure expertise. GitOps for every environment. SLOs, not alerts.

◆ Container platform

Kubernetes (EKS / AKS / GKE) · Helm · Kustomize

◆ Service mesh

Istio · mTLS · traffic shaping · circuit breaking · Envoy proxy

◆ GitOps

ArgoCD · Terraform · Crossplane · infrastructure as code

◆ Platform layer

Backstage IDP · service templates · golden paths · runbooks.

04 AI Engineering

AI & LLM Integration as First-Class Architecture

AI embedded as specific, versioned functional components within enterprise systems not a wrapper around a model API bolted to a UI. Decision engines at approval steps. LLM pipelines replacing document data entry. RAG systems over enterprise knowledge bases. AI agents orchestrating exception workflows. Each component exposes a clear interface, is versioned independently, and has its own drift monitoring and rollback path.

◆ LLM integration

OpenAI API · Anthropic Claude · Bedrock · Azure OpenAI · Vertex AI

◆ RAG architecture

LlamaIndex / LangChain · Pinecone / Weaviate · semantic chunking

◆ Agents

LangGraph · AutoGen · tool-use patterns · human-in-loop checkpoints

◆AI MLOps

MLflow · LangSmith · Evidently AI · shadow deployment · A/B evals

05 Modernisation Engineering

Legacy Modernisation & Zero-Downtime Migration

Replacing production enterprise systems without business disruption requires a migration strategy, not a rewrite plan. Strangler fig pattern new capability built as modern services, traffic migrated incrementally, legacy decommissioned when redundant. Before any code is written, AI-assisted analysis of the legacy codebase surfaces undocumented business logic, high-risk components, and missing test coverage the knowledge that would be lost without it.

◆ Migration pattern

Strangler fig · branch-by-abstraction · parallel run · dark launch

◆ Code analysis

AI-assisted legacy analysis · GitHub Copilot Enterprise · SonarQube

◆ Data migration

CDC with Debezium · dual-write pattern · zero-downtime schema changes

◆ Test safety net

AI-generated test coverage · characterisation tests · contract tests

06 Portal & Workflow Engineering

Enterprise Portals, Workflow Platforms & Automation

Self-service portals and workflow platforms that give every stakeholder employees, suppliers, partners, customers the right access to the right processes, with full audit trails and role-based controls. Built on event-driven foundations so workflows compose rather than fragment. AI embedded at decision and triage points to reduce manual intervention without removing human accountability.

◆ Frontend

React / Next.js · micro-frontend architecture · design system

◆Workflow engine

Temporal · Camunda · custom state machine · event-driven orchestration

◆Access control

Attribute-based access control (ABAC) · audit log · SSO / SAML

◆ AI at workflow

Decision engines at approval steps · LLM document handling · agents

Trusted by Leading Enterprises

Primary Model

98.1% Accuracy • Core Product

Precision

98.5%

Recall

97.8%

Model: Neural Network v3

Features: 180 • Train: 3M rows

Secondary Analysis

92.4% Accuracy • Insights

Model: Decision Tree

Processing: Real-time

Service Performance

<1.5s Latency • API

Uptime

99.99%

Scaling: Auto-scaling

Global Availability

Reference architecture

How enterprise services connect at scale.

        
          
            
          
        
        
        
        
        
        
        
        
        
        
        
        
        
        
      Client Tier
Web App
Mobile
Partners
API Gateway
Kong · rate-limit · JWT verify
Auth Service
OAuth2 · SAML · RBAC
ISTIO SERVICE MESH · mTLS · CIRCUIT BREAKING · LOAD BALANCING
Domain Services
✦ AI Services
core-domain-svc
business logic · DDD
PostgreSQL · event store
workflow-engine-svc
Temporal · state machine
approvals · routing
integration-svc
Kafka · outbox pattern
schema registry · ACL
notification-svc
email · sms · webhook
templating · tracking
✦ ai-inference-svc
LLM · decision engine
RAG · agents
KAFKA EVENT BUS · topics · consumer groups · schema registry · exactly-once semantics
domain events · integration events · CDC events
Data Layer
PostgreSQL
Redis Cache
Elasticsearch
Snowflake DWH
Object Storage
✦ Vector DB
(Pinecone)

AI integration patterns

patterns for embedding AI in enterprise systems.

AI Patterns Grid

Pattern 01 · Decision Engine

ML Model at Approval Workflow

ML model exposed as an internal service. Called synchronously at decision points purchase approval, credit assessment, resource allocation. Returns a scored recommendation; human retains the decision authority.

// approval-workflow.ts const decision = await aiDecisionSvc.score({ context: "purchase_approval", payload: orderRequest, threshold: 0.85 }); if (decision.confidence < threshold) await workflow.routeToHuman(decision);

Pattern 02 · RAG Pipeline

LLM over Enterprise Knowledge Base

Documents, policies, and system data chunked, embedded, and stored in a vector database. LLM retrieves relevant context at query time. Answers are grounded in current enterprise data, not model training data.

// enterprise-rag.py chunks = retriever.query( query=user_question, k=8, filter={"domain":"procurement"} ) response = llm.generate( system=GROUNDING_PROMPT, context=chunks, user=user_question )

Pattern 03 · Document Intelligence

LLM Extraction Replacing Data Entry

Unstructured documents invoices, contracts, reports passed through an LLM extraction pipeline. Structured output validated against a schema, confidence-scored, and routed: high-confidence auto-posted; low-confidence to human review queue.

Lending · Compliance

Core Systems

Origination platformCompliance systemKYC platform

Key Integrations

Core bankingCredit bureauxRegulatory APIs

✦ AI Components

Credit decision engineFraud scoringDoc intelligence KYC

How we work

From domain model to production system.

PHASE 01

Architecture & Domain Modelling

Event storming

Domain events, commands, aggregates, bounded contexts mapped with stakeholders

Integration topology

API contracts, event schemas, data ownership boundaries defined before build

Context mapping

Inter-domain relationships, shared kernel, ACL, open-host service

AI placement

AI components identified, interfaces designed, rollback paths specified

PHASE 02

Foundation & Platform

Infrastructure as code

Terraform, Kubernetes, service mesh, secrets management

Observability stack

OpenTelemetry, Datadog/Grafana, SLOs defined before services ship

CI/CD pipelines

GitHub Actions, ArgoCD, environment promotion, automated rollback

Security baseline

mTLS, Vault secrets, SAST/DAST in pipeline, dependency scanning

PHASE 03

Iterative Service Delivery

Domain service per sprint

Each bounded context delivered independently with its own test suite

Feature flags

LaunchDarkly, trunk-based development, no long-lived branches

Contract testing

Pact, consumer-driven contracts, no integration surprises

AI components

Shadow deployment, A/B evaluation, MLflow experiment tracking

PHASE 04

Production & Continuous Improvement

SLO-based ops

Error budget tracking, alert on SLO breach, not symptoms

Capacity planning

KEDA autoscaling, cost governance, rightsizing reviews

AI monitoring

Model drift detection, Evidently AI, LangSmith eval harness

Knowledge transfer

Architecture decision records, runbooks, on-call handover

Technology reference

The enterprise engineering stack.

✦ AI / LLM

OpenAI GPT-4oAnthropic ClaudeAWS BedrockLangChainLangGraphLlamaIndexAutoGenPineconeWeaviateMLflowLangSmith

Frontend

React / Next.jsTypeScriptAngularVue.jsReact NativeFlutterGraphQLMicro-frontends

Backend

Node.jsPythonJava / Spring Boot.NET / C#GogRPCRESTGraphQL Federation

Integration

Apache KafkaKong API GatewayMuleSoftRabbitMQAWS EventBridgeAzure Service BusDebezium (CDC)Schema Registry

Workflow

TemporalCamundaAirflown8nPrefectStep FunctionsDagster

Data

PostgreSQLMongoDBSnowflakeRedisElasticsearchCassandraDatabricksDelta LakeClickHouse

Platform / Infra

KubernetesIstioTerraformHelmArgoCDCrossplaneAWS / Azure / GCPVaultBackstage

Observability

OpenTelemetryDatadogGrafanaJaegerPrometheusPagerDutySonarQubeKEDA

Where these capabilities apply

Edge-to-Cloud AI across our platforms and services.

Development

Full-Stack Development

Build scalable web applications across front-end, back-end, APIs, databases, and cloud infrastructure.

→ full-stack-development

Development

Web & Mobile App Development

Create responsive web and mobile applications designed for performance, usability, scalability, and evolving business needs.

→ web-mobile-app-development