Three practices. One delivery standard.
Whether the engagement is a CRM rebuild, a customer platform, or an AI programme, the same discipline applies: design first, build deliberately, test relentlessly, document everything.
CRM and Salesforce Platforms
Customer platforms that sales, service, and leadership actually trust: architected, built, and governed end to end.
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Sales Cloud
We design and build complete Sales Cloud environments from the ground up: pipeline architecture, account and territory management, lead-to-opportunity workflows, forecasting models, and custom UI components that help sales teams move faster and close more. Every build is shaped around how your sellers actually work, not how Salesforce works out of the box.
- Pipeline architecture
- Territory management
- Workflows and forecasting
- Custom UI components
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Service Cloud
End-to-end Service Cloud implementations that change how support teams operate: case management frameworks, intelligent support flows, escalation logic, SLA tracking, and omni-channel routing that handles voice, email, chat, and social in one agent experience. We also build knowledge bases, agent productivity tools, and reporting that gives managers real visibility into team performance.
- Case management framework
- Omni-channel routing
- Escalation and SLA logic
- Agent productivity tools
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Experience Cloud
Branded digital portals, partner communities, and customer self-service experiences on Experience Cloud, including authenticated user journeys, guest-access flows, and community-driven engagement models. Whether it is a support portal, a partner enablement hub, or an employee community, we build experiences that are secure, scalable, and aligned to your brand.
- Branded portals and hubs
- Authenticated journeys
- Secure guest access
- Self-service communities
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AI and Agentforce
We implement Salesforce's Agentforce platform to deploy autonomous AI agents that handle real service and sales interactions: intelligent case routing, automated responses, and action execution across connected systems. We also handle prompt engineering for grounded, enterprise-safe interactions, Einstein AI configuration, and model strategy for industry-specific compliance and response quality.
- Agentforce deployments
- Autonomous agent flows
- Grounded prompt engineering
- Einstein compliance
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Data Cloud
Unified customer data architectures on Salesforce Data Cloud: ingesting, harmonising, and activating data from fragmented sources into a single, real-time customer profile. This includes segmentation, identity resolution, real-time activation from live signals and event-driven triggers, and zero-copy integration patterns that connect your data without duplicating it across systems.
- Customer data architecture
- Fragmented source ingest
- Identity resolution rules
- Zero-copy integrations
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Integrations and DevOps
The integration and release infrastructure that makes enterprise Salesforce delivery sustainable: REST and SOAP API patterns, MuleSoft middleware architecture, CI/CD pipelines on GitHub and Flosum, four-tier sandbox lifecycle management, and release governance that gives your team full control over what goes to production, when, and how safely.
- REST and SOAP APIs
- MuleSoft architecture
- CI/CD with GitHub and Flosum
- Sandbox lifecycle support
Digital Platforms and Engineering
Bespoke web platforms, high-performance backends, and the cloud infrastructure that keeps them fast at scale.
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React and Next.js
Modern, high-performance web applications and enterprise portals using React and Next.js, from single-page applications and customer-facing dashboards to complex internal tools that need real-time data, role-based access, and clean integration with backend APIs or Salesforce. Our frontend builds are component-driven, fully typed with TypeScript, and built with performance and maintainability as first-class requirements.
- High-performance web apps
- React and Next.js frameworks
- TypeScript typing
- Real-time access control
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Node.js and Express
Scalable backend APIs and microservices using Node.js and Express, handling authentication, business logic, third-party integrations, and the data transformation layers that sit between your frontend applications and your core systems. Our Node builds are designed for production from day one, with structured error handling, logging, rate limiting, and documentation that make them easy to maintain and extend.
- Scalable Express APIs
- Decoupled business logic
- Rate limiting and security
- Robust microservices
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Python
We use Python across backend and data engineering work: automation scripts, data ingestion pipelines, ETL workflows, REST APIs with FastAPI or Flask, scheduled jobs, and integration connectors that tie disparate systems together. Python is also our primary language for machine learning, which makes it a natural bridge between engineering and AI within a single engagement.
- ETL data pipelines
- FastAPI and Flask APIs
- Automation and scaling jobs
- Engineering to AI bridge
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Java and Spring Boot
Enterprise-grade backend systems using Java and Spring Boot: robust REST APIs, service layers, and integration components that meet the reliability and security standards large organisations expect. Java is particularly well suited to high-throughput, compliance-sensitive environments where thread safety, strong typing, and long-term maintainability matter more than development speed alone.
- Spring Boot frameworks
- Compliance and threat safety
- Highly typed API security
- Enterprise service layers
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Database and Cloud
The data and infrastructure layer that underpins modern applications: relational databases like PostgreSQL and MySQL, document stores like MongoDB, and cloud infrastructure across AWS and GCP. We handle schema design, query optimisation, data migration, environment configuration, and the infrastructure-as-code patterns that make cloud environments reproducible, secure, and cost-efficient at scale.
- PostgreSQL and Mongo stores
- AWS and GCP architectures
- Query optimisation schemes
- Infrastructure as code
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DevOps and CI/CD
The automated delivery pipelines that let teams ship software confidently and consistently: Docker for containerisation, GitHub Actions for CI/CD, automated testing frameworks as quality gates, and environment management that removes the "works on my machine" problem. DevOps practice is not an afterthought in our engagements; it is built into the delivery model from the start.
- Delivery automation paths
- Docker containerisation
- GitHub Actions and pipelines
- Quality gates and testing
AI, Automation and Data
Applied AI, intelligent automation, and the data foundations that turn information into a working advantage.
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AI Implementation
Custom AI solutions built for real business problems: large language model integration using OpenAI, Anthropic, and open-source models, retrieval-augmented generation (RAG) architectures for grounded enterprise responses, and AI features embedded directly into web applications or Salesforce workflows. We handle the full lifecycle, from use-case definition and model selection through deployment, monitoring, and ongoing improvement.
- LLM grounded integrations
- Custom RAG architectures
- OpenAI and Anthropic models
- Deployment and verification
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Python ML Pipelines
End-to-end machine learning pipelines in Python: data ingestion and preprocessing, feature engineering, model training and evaluation with TensorFlow, PyTorch, and scikit-learn, and inference workflows that serve predictions to production applications in real time. We also handle model versioning, experiment tracking, and the retraining pipelines that keep models accurate as data changes over time.
- Ingestion and preprocessing
- TensorFlow and PyTorch models
- Real-time prediction flows
- Retraining pipelines
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Intelligent Automation
We identify and automate the high-volume, rule-based, and increasingly judgment-requiring processes that consume disproportionate time in enterprise operations, using AI-driven workflow orchestration, RPA patterns, and intelligent document processing to remove manual bottlenecks. The result is not just faster processes but smarter ones: systems that learn, adapt, and escalate to humans only when genuinely necessary.
- Workflow orchestration
- Intelligent document processing
- Process bottleneck removal
- Smart autonomous escalation
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Data Engineering
The data infrastructure that turns raw information into reliable, queryable, actionable assets: ETL and ELT pipelines, data warehouse design on BigQuery, Redshift, or Snowflake, real-time streaming with Kafka or event-driven patterns, and analytics layers that connect to BI tools. Good data engineering is what makes AI and reporting actually trustworthy.
- ELT and ETL data pipelines
- BigQuery, Redshift, Snowflake
- Real-time Kafka streaming
- Analytics and BI integrations
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API and Integration Layer
Integration architecture that connects your systems cleanly and reliably: REST and GraphQL APIs, WebSocket connections for real-time communication, event-driven architectures using message queues and pub/sub patterns, and webhook frameworks that keep data in sync across platforms without tight coupling. Well-designed integrations are invisible to end users and invaluable to the engineers maintaining the system long term.
- REST and GraphQL gateways
- WebSockets and event queues
- Loose platform coupling
- Long-term maintainability
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Agentforce and Einstein
Salesforce-native AI through Agentforce and Einstein: autonomous agent flows that reason, retrieve, and act across your CRM data without external AI infrastructure. This includes configuring agent topics and actions, engineering grounded prompts that stay within policy boundaries, connecting agents to external knowledge sources, and designing the fallback and escalation logic that keeps autonomous behaviour safe and auditable.
- Agentforce configuration
- CRM grounded actions
- Einstein guardrail policy
- Fallback and escalation logs
Fluent across the modern enterprise stack.
See it in the work-
CRM and cloud platform
Enterprise tenant administration, custom Lightning development, autonomous workflows, and low-latency synchronisation.
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Frontend
High-fidelity responsive user interfaces, structured modular web portals, and polished, optimised layouts.
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Backend
Secure application backends, fast and scalable microservices, custom endpoints, and data processing nodes.
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Data and AI
Grounded autonomous LLM flow models, cognitive prompts, semantic database schemas, and intelligent analytics.
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DevOps and infrastructure
Automated deployment, high-isolation dev branches, unified sandbox security pipelines, and zero-downtime server scaling.
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Security and compliance
Security-first operations, least-privilege permission schemas, and rigorous automated code analysis.
Six phases, zero surprises.
Our release governance pipeline moves code from sandbox to production with the predictability that regulated industries demand, and every team deserves.
- Discover
Discovery and systems audit
Detailed analysis of your current systems, data structures, bottlenecks, and core business goals.
- Design
Architecture and blueprint
Designing ERD models, security matrixes, FFLIB structures, and integration patterns before coding.
- Build
Disciplined development
Clean Apex, high-performance LWCs, declarative flows, and structured API integrations.
- Validate
Rigorous code quality
Static code analysis, 90%+ unit test coverage, security audits, and formal UAT validation.
- Deploy
Release and governance
Sandbox progression, Flosum or GitHub pipelines, and production release sanity validations.
- Support
Hyper-care and optimisation
Operational support, performance tracing, certificate rotation, and optimisation audits.
Tell us what you are trying to build.
Bring us the messy diagram, the stalled migration, or the AI idea nobody has scoped yet. The first conversation is a working session, not a sales call.