Connect your organisation to the AI platforms that fit, and make them work on your data.
Synapsys delivers platform integration with market leaders like Claude, Gemini, and Azure OpenAI, plus custom model work through RAG and fine-tuning grounded in your company's knowledge. Structured, governed delivery after your AI Readiness Audit.
Not a chatbot pilot. Not a single workflow agent. Not a greenfield app. Enterprise AI infrastructure your teams can build on.
Which service fits?
Implement AI platforms and models, or something else?
AI Implementation is the governed transformation step after your AI Readiness Audit. For automated workflows or agent builds, see AI Agents & Automation. For a full software product, see AI Native App Development.
This service
AI Implementation
Synapsys connects your organisation to proven AI platforms and builds custom intelligence on your data as a structured programme, not a one-off experiment.
Part of the AI Transformation pillar, after your readiness assessment
Platform integration: Claude, Gemini, Azure OpenAI, AWS Bedrock, and more
Custom models: RAG and fine-tuning on your company data and industry knowledge
Enterprise-wide capability with governance, security, and observability
Foundation layer that agents, apps, and teams build on
The governed step that turns your assessment into production-grade AI capability
Your readiness audit identified where AI creates value. Implementation makes it real: connecting platforms, building models on your data, and establishing the infrastructure your organisation runs on.
You assessed. Now you implement.
This service follows your AI Readiness Audit. Your prioritised roadmap, governance framework, and platform recommendations become a production deployment, not another planning cycle.
Platform integration, not platform shopping
We connect you to market leaders (Claude, Gemini, Azure OpenAI, AWS Bedrock) with SSO, API gateways, guardrails, cost controls, and environment separation built in from the start.
Custom intelligence on your data
RAG and fine-tuning so AI answers from your policies, product catalogues, contracts, and industry context, not generic training data that knows nothing about your business.
Not agents. Not apps. The layer underneath both.
AI Agents & Automation builds workflows on top of your stack. AI Native App Development builds software products. This service delivers the platform and model layer both depend on.
What we implement
Five capabilities that make AI work for your organisation.
Platform Integration
Hook into the AI platforms your assessment recommended.
We integrate your organisation with market-leading AI platforms, not as disconnected experiments, but as governed, production-ready infrastructure your teams can access securely and consistently.
Claude, Gemini, Azure OpenAI, AWS Bedrock, and other leading providers
API gateway with rate limiting, cost tracking, and environment separation
SSO and role-based access aligned with your identity provider
Prompt templates, guardrails, and usage policies enforced centrally
Integration hooks for CRM, ERP, email, and internal business systems
Custom Models (RAG)
AI that answers from your documents, not the internet.
Retrieval-augmented generation connects large language models to your company's knowledge base. Staff and customers get accurate, cited answers grounded in your actual policies, products, and records.
Ingestion from PDFs, wikis, SharePoint, databases, and internal repositories
Cited, source-linked responses, not hallucinated guesses
Department and role-based access controls on sensitive content
Continuous index refresh as documents and data change
PDPA-aligned data handling with clear data residency options
Model Fine-Tuning
When retrieval is not enough, train on your domain.
For specialised terminology, consistent output formats, or proprietary reasoning patterns, fine-tuning adapts a base model to your industry and use case, with evaluation benchmarks that prove it works before go-live.
Training data curation and quality review with your subject-matter experts
Domain-specific fine-tuning for legal, financial, engineering, and healthcare contexts
Evaluation benchmarks against your acceptance criteria
A/B comparison between base model, RAG, and fine-tuned approaches
Model versioning and rollback capability for safe updates
Data Pipeline & Knowledge Architecture
The plumbing that makes AI reliable at scale.
RAG and fine-tuning only work with well-structured data. We design ingestion pipelines, chunking strategies, embedding models, and vector store architecture so your AI stays accurate as your knowledge base grows.
Document ingestion pipelines with format detection and OCR where needed
Chunking and embedding strategy tuned to your content types
Vector store selection and architecture (Pinecone, Weaviate, pgvector, Azure AI Search)
Scheduled refresh and incremental update pipelines
Data lineage tracking so you know what the model was trained and queried on
Governance, Security & Observability
Production AI needs guardrails, not just capability.
Enterprise AI deployment requires more than an API key. We implement the security, monitoring, and governance layer that keeps your AI reliable, compliant, and accountable after go-live.
Prompt injection defence and output filtering
Audit logging of queries, responses, and model decisions
Cost and usage monitoring with alerts on anomalies
EU AI Act and PDPA alignment documentation
Model performance dashboards with accuracy and latency tracking
Who this is for
For organisations ready to move from assessment to production-grade AI.
This service works best after your AI Readiness Audit, when you have a prioritised roadmap and leadership alignment on investment.
CIO and CTO post-assessment
Completed the AI Readiness Audit and need a governed partner to roll out the platform and model layer your roadmap recommends.
Data and knowledge-heavy organisations
Legal, financial services, manufacturing, and professional services with proprietary content that generic AI cannot answer from.
IT leaders managing AI sprawl
Multiple teams experimenting with different tools and API keys. You need one governed platform layer with centralised access, cost control, and security.
Companies scaling beyond pilots
Proven POCs that impressed in a demo but lack production infrastructure, governance, or integration with enterprise systems.
Not sure if you are the right fit? Talk to us. If you have not assessed readiness yet, start with the AI Readiness Audit.
How we deliver
From assessment findings to production AI infrastructure.
Every implementation follows a structured delivery process grounded in your readiness report. Synapsys owns the programme. Platform engineering, data pipeline work, and model development are handled by our qualified partner network, selected= for your specific platform and data requirements.
01
Step 01
Discovery & platform assessment
Validate audit findings, confirm data sources and quality, assess platform fit, and define success criteria with business owners.
02
Step 02
Architecture & model selection
Vendor-neutral decision record: API platform vs RAG vs fine-tune. Integration topology, security model, and cost projections documented before build.
03
Step 03
Build & integrate
Platform connection, data pipeline construction, model deployment, and enterprise system integration. Weekly status updates throughout.
04
Step 04
Validate & harden
Red-team testing, accuracy benchmarks, load testing, security review, and UAT with business owners before production cutover.
05
Step 05
Go-live & handover
Production deployment, documentation, team training, and hypercare period. Clear path to AI Managed Services for ongoing operations.
Suited for
Enterprise use cases we implement most often.
Every programme is scoped to your environment and audit roadmap. These are the implementation patterns we deliver most frequently for mid-market companies in Malaysia and Southeast Asia.
Enterprise knowledge assistant
Organisation-wide AI that answers from policies, SOPs, product docs, and internal wikis, with department-level access controls.
Document intelligence
AI-powered search, summarisation, and extraction across contracts, policies, technical manuals, and regulatory filings.
Customer-facing AI on your data
Support and sales AI grounded in your product catalogue, pricing rules, and service history, not generic internet knowledge.
Industry-specific models
Fine-tuned or RAG systems for specialised terminology in legal, healthcare, engineering, and financial services.
Multi-language ASEAN deployment
Single platform layer supporting Bahasa Malaysia, English, Mandarin, and other regional languages across departments.
Copilot rollout for internal tools
AI assistance embedded in email, CRM, ERP, and productivity tools through a governed central platform.
Why Synapsys
Governed implementation from a partner who owns the full transformation journey.
Vendor-neutral platform selection
No reseller agreements or preferred vendor deals. We recommend the platform and model approach that fits your audit findings, data, and budget, and document why.
Assessment-to-implementation continuity
The same partner who produced your Synapsys IQ readiness report delivers the implementation. No context lost, no re-discovery phase, no conflicting recommendations.
European governance standards
Structured scoping, change control, UAT, and documentation. Built for regulated environments and organisations that need accountability, not experimentation.
Operate what we implement
AI Managed Services continues with the team that built your platform layer. Monitoring, optimisation, and governance without a handoff to strangers.
Related services
Where this sits in the Synapsys journey.
Your readiness report feeds directly into implementation. Each downstream service builds on what we learned, without starting from scratch.
Assess
AI Readiness Audit
Vendor-neutral assessment, scored report, and prioritised roadmap: the step before implementation.
Tell us where you are in your AI Transformation journey and what your readiness report recommended. We will tell you honestly whether implementation is the right next step. No pitch, no commitment.
Yes, in almost every case. The AI Readiness Audit (Synapsys IQ) produces a scored readiness report, prioritised roadmap, platform recommendations, and governance framework: the inputs implementation is designed around. Your report feeds directly into discovery and architecture, so we do not repeat that work from scratch. If you completed a comparable assessment elsewhere, we begin with a paid discovery engagement to validate findings before any build work starts.
AI Implementation delivers the enterprise platform and model layer: connecting to Claude, Gemini, or Azure OpenAI, building RAG on your data, and establishing governance. AI Agents & Automation builds specific agents and workflows on top of that stack: a tactical project scoped to one process. Many clients do both: implementation establishes the foundation, then agents automate day-to-day work.
Platform API alone works when general knowledge is sufficient and your use case does not require proprietary data. RAG is the right choice when AI must answer from your documents and records with cited sources. Fine-tuning is warranted when you need consistent domain-specific language, output formats, or reasoning patterns that retrieval cannot reliably produce. We recommend the approach during architecture, based on your readiness report and agreed acceptance criteria.
We are vendor-neutral. Platform options (Claude, Gemini, Azure OpenAI, AWS Bedrock, and open-source models where appropriate) are usually shortlisted in your readiness report and confirmed in an architecture decision record, with rationale, cost projections, and fallback options.
Data handling is designed into the architecture from discovery. Governance requirements identified in your readiness report inform access controls, retention policies, and residency decisions. We document data flows end to end. RAG indexes can be scoped to exclude sensitive data. Fine-tuning datasets are curated with your legal and compliance teams. All implementations include audit logging for accountability.
You do. Your data remains yours. Fine-tuned model weights, RAG indexes, prompt libraries, and integration configurations are handed over at go-live with full documentation. We do not retain ownership or create vendor lock-in through proprietary formats.
Most clients transition to AI Managed Services for ongoing monitoring, model updates, cost optimisation, and governance. Others hand operations to internal IT with our documentation and training. We support both paths and define the handover plan during go-live, building on the same readiness report and architecture your implementation started from.
