Technologies

The Right Technology for the Right Problem

Costcone brings deep Microsoft 365 and Power Platform expertise — built over years of enterprise implementation — combined with a modern open source delivery capability and a growing suite of AI integrations. Suitability, usability, and value are the criteria. The best tool wins.

Technology Capability

Three capability areas. One coordinated approach.

Costcone operates across three technology domains — each mature in its own right, and designed to work together where the engagement demands it. No allegiance to any single vendor. No unnecessary complexity. Every technology decision is made in the context of the client's existing environment, operational maturity, and available budget.

Microsoft 365 & Power Platform

SharePoint · Teams · Power Apps · Power Automate · Power BI · Copilot

Open Source & Modern Stack

React · Supabase · Node.js · Python · Deno · PostgreSQL

AI & Intelligent Layer

GPT-4o · Gemini · Claude · Copilot · Custom fine-tuned models

Specialist Capability

Microsoft 365 & Power Platform

Enterprise-grade implementation. Built over years of practice. Costcone's roots are in Microsoft 365. Over years of client implementations — from document management architecture to enterprise governance programmes — the team has developed deep, practitioner-level expertise across the full Microsoft ecosystem. This is not a licensing relationship. It is implementation knowledge earned from complex, real-world deployments.

Core Specialism

SharePoint & Document Management

Enterprise document management, metadata architecture, retention policies, versioning, permissions, and search — implemented to the standard that makes AI integration viable.

SharePoint Online — site architecture, document libraries, metadata taxonomy, content types
SharePoint Server — on-premise and hybrid deployments, migration to Online
Retention & compliance — Information Protection, Records Management, data lifecycle governance
Search & discoverability — enterprise search configuration, metadata-driven navigation
Permissions architecture — role-based access, inheritance design, sensitivity labelling

Productivity & Collaboration

Microsoft 365 Suite

Full Microsoft 365 implementation and governance — Teams architecture, Exchange configuration, OneDrive policy, and cross-platform governance ensuring the suite operates as a coherent system.

Microsoft Teams — structure design, governance policies, meeting room integration, external access
Exchange Online — mailbox configuration, retention, shared mailbox governance, mail flow rules
OneDrive for Business — sync policies, known folder move, sharing controls
Microsoft Purview — data classification, compliance scores, eDiscovery, audit logs
Microsoft Intune — device management, app protection policies, conditional access

Low-Code Automation & Intelligence

Power Platform

End-to-end Power Platform delivery — from citizen developer enablement to enterprise-scale automation. Solutions that deliver measurable operational efficiency without the cost of full custom development.

Power Apps — model-driven and canvas apps, custom connectors, component libraries
Power Automate — approval workflows, document processing automation, system integration flows
Power BI — data models, executive dashboards, self-serve analytics, embedded reporting
Power Pages — external-facing portals, partner and customer engagement platforms
Microsoft Copilot Studio — enterprise chatbot and AI agent development within M365

Enterprise Workflow Tooling

Third-Party Workflow Automation

For organisations requiring workflow automation beyond Power Automate — or with existing investments in specialist platforms — Costcone implements and integrates leading workflow toolsets.

Nintex — advanced workflow automation, document generation, forms, and process mapping
K2 (now Nintex Workflow Cloud) — enterprise workflow orchestration and business rules
Third-party connectors — integration with ServiceNow, Jira, Salesforce, and custom APIs

AI Within the Microsoft Ecosystem

Microsoft Copilot & AI Services

Costcone implements Microsoft Copilot and Azure AI services for organisations whose governance, data residency, or licensing strategy makes the Microsoft AI ecosystem the right choice. This includes readiness assessment, governance configuration, and adoption programmes.

Microsoft 365 Copilot — readiness assessment, data governance pre-work, prompt engineering, adoption rollout
Azure OpenAI Service — enterprise GPT deployment within Azure infrastructure, data sovereignty preserved
Copilot Studio — custom Copilot agents, knowledge base integration, Teams and SharePoint embedding
Azure Cognitive Services — document intelligence, form recognition, vision, and language services
AI Builder — Power Platform-native AI — document processing, prediction models, sentiment

Modern Delivery Stack

Open Source & Modern Stack

Built for the way modern organisations actually work. As delivery requirements evolved — and as the gap between enterprise cost and SME budget widened — Costcone systematically built capability in the modern open source stack. The Costcone Enterprise Intelligence Platform itself runs on this stack — demonstrating that production-grade, board-level intelligence infrastructure does not require enterprise licensing. It requires engineering discipline.

User Experience & Delivery

Frontend & Application Layer

Modern, component-based frontend development — built for performance, accessibility, and long-term maintainability.

React 18 + TypeScript — component architecture, type-safe delivery, scalable state management
Vite — fast build tooling, HMR development environment, optimised bundling
Tailwind CSS + shadcn/ui — utility-first styling, design system implementation, accessible components
TanStack Query — server-state management, caching, real-time synchronisation
Next.js — server-side rendering, static generation, edge deployment for public-facing products

Reliability & Governance

Backend & Data Infrastructure

PostgreSQL-based data architectures with multi-tenant isolation, row-level security, and enterprise governance controls — without vendor lock-in.

Supabase — managed PostgreSQL, edge functions, auth, real-time subscriptions, storage
PostgreSQL — schema design, RLS policies, triggers, functions, performance optimisation
Deno runtime — edge function execution, TypeScript-native, zero-configuration deployment
Node.js — API services, background jobs, integration middleware, webhook handling
Redis — caching layer, rate limiting, session management, queue management

Pipelines & Governance

Data Management & Integration

Data architecture and pipeline delivery — ETL pipelines, API integrations, data quality frameworks, and the governance layer that makes data trustworthy enough to feed AI systems.

Python — data processing, ETL pipelines, scripting, ML model integration
dbt — data transformation, modelling, testing, and documentation in SQL
Apache Airflow — workflow orchestration, scheduled pipelines, dependency management
Pandas / Polars — data wrangling, cleaning, transformation, exploratory analysis
REST & GraphQL — API design, integration architecture, connector development

Cloud-Native Delivery

Deployment & Infrastructure

Cloud-native deployment across major providers — with a preference for serverless and edge architectures that reduce operational overhead and scale with demand.

Vercel / Netlify — frontend deployment, edge delivery, preview environments, CI/CD
Supabase Cloud — managed backend infrastructure, global edge functions
AWS — EC2, Lambda, S3, RDS, CloudFront — for clients with existing AWS investment
Azure — App Service, Functions, Blob Storage — aligned with M365/SharePoint environments
Docker + GitHub Actions — containerised workloads, CI/CD pipelines, automated testing

AI Integration Layer

AI that is embedded, governed, and grounded in your data.

Costcone does not implement AI as a feature. It is implemented as an intelligence layer — grounded in real organisational data, governed by the same policies that govern the rest of the environment, and selected on the basis of what is appropriate for the specific use case, data classification requirements, and the budget available.

OpenAI GPT-4o / GPT-4 Turbo

Language models — general intelligence

Use: Document analysis, narrative synthesis, intelligent search, content generation, classification, and structured data extraction.

Fit: Best for complex reasoning, long-context analysis, structured output generation. Appropriate where data can leave the client environment or Azure OpenAI Service is in scope.

Google Gemini (1.5 Pro / Flash)

Language models — multimodal

Use: Long-context processing, document understanding, multimodal analysis (text + image + data), and high-volume inference workloads.

Fit: Best for large document corpora, high-volume workloads, multimodal inputs. Flash variant for latency-sensitive applications.

Anthropic Claude (Sonnet / Haiku)

Language models — precision and safety

Use: Complex reasoning tasks requiring careful, structured outputs with low hallucination.

Fit: Best for compliance documentation, risk analysis, structured extraction. Haiku for lightweight classification and routing.

Microsoft Copilot & Azure OpenAI

Enterprise AI — within the M365 boundary

Use: AI capability within Microsoft-governed environments — data residency, Entra ID integration, or existing Azure investment.

Fit: Best for organisations with M365 licensing, data sovereignty requirements, or existing Azure infrastructure.

Open Source LLMs (LLaMA, Mistral, Phi)

Self-hosted models — data privacy and cost

Use: On-premise or private cloud model deployment where data cannot leave the client environment under any circumstances.

Fit: Best for regulated sectors, confidential data, high-volume low-complexity tasks, zero per-token cost at scale.

RAG Architecture

Grounded AI — retrieval-augmented generation

Use: The foundational AI architecture pattern used where AI answers questions from an organisation's own data.

Fit: Essential for internal Q&A systems, policy search, document-grounded decision support. Requires well-structured, governed data.

Vector Databases (pgvector, Pinecone, Weaviate)

Semantic search infrastructure

Use: Enabling AI systems to retrieve the most relevant content from large document corpora.

Fit: Best for any RAG deployment, enterprise search, recommendation systems. pgvector preferred where PostgreSQL is already in use.

Technology Selection

Four principles. Applied to every decision.

Technology selection at Costcone is not driven by vendor relationships, certification requirements, or what is generating the most industry attention. It is driven by four consistent principles — applied in order, without exception.

Suitability before sophistication

The most technically impressive solution is not automatically the right one. Before any technology is proposed, Costcone evaluates whether it is genuinely suited to the problem — taking into account the organisation's technical maturity, the capability of the team who will maintain it, and the realistic complexity of the requirement.

Usability is a delivery outcome

A system that users do not adopt has failed — regardless of how well it was built. Costcone treats usability as a first-class delivery criterion, not a design afterthought. This means involving end users in scoping, building with established design systems, and measuring success against actual adoption rates rather than go-live dates.

Budget is a constraint, not an afterthought

Technology recommendations are made with total cost of ownership in mind — licensing, infrastructure, implementation, training, and ongoing maintenance. Costcone presents both options with honest cost modelling and lets the client make an informed decision. The recommendation is always the best value option, not the highest-margin one.

AI augments. It does not replace governance.

AI tools are selected and deployed only where the underlying data is structured, governed, and trustworthy enough to produce reliable outputs. Deploying AI on top of poor data governance produces confident, fast, wrong answers. The diagnostic and governance work always precedes the AI layer.

Microsoft 365 + AI

Existing Microsoft investment. Intelligently extended.

For organisations with established Microsoft 365 environments, Costcone offers a structured coordination model — connecting the governance and structure of the M365 ecosystem to the intelligence capability of modern AI. This is not a migration away from Microsoft. It is a programme of extending what already exists.

The M365 FoundationThe AI Extension

SharePoint document governance — metadata, retention, classification — becomes the knowledge base that AI queries

RAG pipelines connect SharePoint document libraries to LLMs — enabling staff to query the organisation's own knowledge in plain language

Power Platform automation connects AI outputs to business processes — approvals, routing, notifications, reporting

Azure OpenAI Service keeps AI within the Microsoft boundary for clients with data sovereignty requirements

Microsoft Purview data classification feeds directly into AI access controls — ensuring AI only queries what users are permitted to see

Custom Copilot agents replace generic chatbot deployments — purpose-built for specific workflows and grounded in client data

Teams becomes the interface layer — Copilot agents, chatbots, and AI-assisted workflows surface where work already happens

AI Builder within Power Platform brings document intelligence, classification, and prediction to existing Power Apps and Automate flows

Power BI integrates AI-generated insights into existing executive dashboards without displacing familiar reporting formats

Where Microsoft AI reaches its limits, open model APIs are introduced through a governed integration layer — not as a replacement, but as an extension

Decision Framework

When we recommend what.

The honest answer to the question clients most frequently ask: given what we already have, and what we are trying to do, what should we actually use? The answer is always contextual — but these are the consistent patterns from years of delivery.

Scenario	Recommended Approach	Why
Document management & knowledge governance	SharePoint Online + Purview	Already licensed in M365, familiar UI, compliance tooling built in
Workflow automation within M365 environment	Power Automate + Power Apps + Nintex	Native integration, no additional infrastructure, citizen developer maintainable; Nintex for advanced orchestration
Executive dashboards and reporting	Power BI or Supabase + custom dashboard	Power BI for M365 environments; custom for EIP-style real-time intelligence
Custom SaaS product or internal tool	React + Supabase + PostgreSQL	Full flexibility, no per-seat licensing, scales independently of M365
AI on top of existing documents	RAG pipeline + SharePoint/OneDrive + LLM API	Requires governance foundation first; then connects existing content to AI query layer
AI within Microsoft boundary (data sovereignty)	Azure OpenAI + Copilot Studio	Data stays in Azure/M365 boundary; Entra ID, Purview controls apply
High-volume AI inference (cost sensitive)	Open source LLM (Mistral / LLaMA) self-hosted	Zero per-token cost at scale; requires infrastructure and model management capability
Regulated / air-gapped AI deployment	On-premise open source LLM	Data never leaves client environment; full control over model and data
Process redesign with automation	Power Automate, Nintex, or Node.js / Python	Low-code preferred for maintainability; custom code where logic exceeds low-code capability
Data pipeline and analytics infrastructure	Python + dbt + PostgreSQL / Supabase	Open, version-controlled, testable; avoids proprietary ETL licensing

Not sure what the right technology is for your situation?

That is exactly the conversation we are built for. Costcone runs technology assessment conversations that are vendor-neutral, budget-realistic, and grounded in what your organisation already has. We will tell you what fits — and what would be a waste of money.