AI Systems Integrator
Put AI into production. Not just proof-of-concept.
We architect, deploy, and operationalise AI pipelines inside your existing infrastructure — bridging the gap between foundation models and real-world business systems.
The Infrastructure Layer AI Projects Actually Need
Most AI initiatives fail not because the model underperforms — they fail because the surrounding infrastructure wasn't engineered to support production workloads. Deploying AI is a systems problem, not a model problem. We solve it end-to-end.
We operate as your technical integration partner, embedding directly into your product or engineering team. Our scope spans the full stack: from API and data layer connectivity through to orchestration, retrieval-augmented pipelines, evaluation frameworks, and observability.
Whether you're deploying a first AI feature or scaling an existing model into enterprise-grade infrastructure, we bring the systems architecture expertise to make it production-ready — on time, on spec, and owned by your team when we're done.
The Difference
Without AI Systems Integrator
- AI prototype works in demos, fails in production
- Model connected to no data — generic, unhelpful outputs
- No visibility into what the model is doing or why
- Team dependent on the vendor who built it
- Locked into one provider — no flexibility
- No way to measure output quality over time
With AI Systems Integrator
- Architecture designed for production workloads from day one
- RAG pipelines grounding responses in your proprietary knowledge
- Structured logging, tracing, and evaluation dashboards
- Full documentation, runbooks, and hands-on knowledge transfer
- Model-agnostic architecture with provider optionality built in
- Evaluation framework with regression testing and quality scoring
Core Services
Systems Architecture
End-to-end AI pipeline design fitted to your existing tech stack and data layer. We define the integration boundaries, data flows, and model interaction patterns before a line of code is written.
LLM Orchestration
Prompt engineering, chaining, routing, and fallback logic for robust production behaviour. We handle multi-step reasoning chains, tool use, and latency optimisation across any model provider.
RAG & Knowledge Systems
Vector store integration, retrieval strategies, chunking pipelines, and grounding logic that reduce hallucination and keep model outputs accurate against your proprietary data.
Observability & Evals
Structured logging, latency tracking, output evaluation frameworks, and regression testing for ongoing quality assurance. You'll know when your AI system degrades before your users do.
Enterprise-Grade Security
Data handling, access controls, and model interaction patterns implemented with enterprise compliance in mind from day one — aligned to NIST and FedRAMP controls for federal clients.
Scalable from SMB to Enterprise
Our engagement model is scoped to your current scale and built to grow. Whether you're a 20-person team or a 2,000-person organisation, the architecture holds.
How We Work — 5-Phase Delivery
A structured engagement model that compresses delivery cycles without cutting corners.
Technical Discovery (Weeks 1–2)
We map your existing data infrastructure, APIs, and system boundaries. We assess model selection options, identify integration risks, and define the full scope. Output: a comprehensive technical brief covering integration architecture, model selection rationale, data flow diagrams, and a risk register.
Architecture Design (Weeks 2–4)
We design the full AI pipeline architecture — orchestration logic, retrieval strategy, data flow, security boundaries, and evaluation approach — reviewed and agreed before a single line of integration code is written. No surprises mid-build.
Integration Build & Testing (Weeks 4–12)
Our engineers implement the pipeline inside your environment. Parallel evaluation runs validate output quality and system performance against agreed benchmarks throughout the build. We test failure modes, edge cases, and latency under realistic load conditions.
Production Deployment (Weeks 12–15)
Controlled rollout with observability tooling in place from go-live. We monitor first-run production behaviour, track evaluation scores against baseline, and resolve any integration edge cases before full handover.
Handover & Documentation (Weeks 15–16)
Complete system documentation, architecture decision records, runbooks, and structured knowledge transfer sessions with your team. Optional retainer support available for ongoing iteration cycles and model upgrades.
Ideal For
Technology Stack
LLM Providers
Orchestration
Vector Stores
Evaluation
Observability
Infrastructure
Security
AI Systems Integrator FAQ
Consultants recommend. We build. We don't deliver architecture diagrams or vendor comparison reports — we write the integration code, configure the pipelines, and deploy to your environment. The output is a running production system with documentation your team can own, not a slide deck with recommendations.
Yes. This is one of our most common starting points. We conduct a technical assessment of your existing prototype, identify the gaps between demo-quality and production-quality (error handling, latency, observability, security, evaluation), and build the infrastructure to close them.
We're model-agnostic by design. We work with OpenAI (GPT-4o, o1), Anthropic (Claude), AWS Bedrock, Azure OpenAI, and open-weight models deployed via Ollama or similar. We'll recommend the right model for your use case and budget — and architect the system so you're not locked in if a better option emerges.
12–16 weeks from Technical Discovery through to Production Deployment and Handover. Smaller, well-scoped integrations can move faster. Complex multi-system RAG pipelines with extensive evaluation requirements may take longer. We scope accurately in Phase 1 and don't move the timeline without your agreement.
It means your AI system isn't hardwired to a single provider's SDK. We abstract the model interaction layer so that switching from OpenAI to Anthropic (or running both in parallel) is a configuration change, not a rewrite. This protects your investment as the model landscape continues to evolve.
Yes. We offer post-handover retainer support for monitoring, model upgrades, evaluation maintenance, and feature iteration. We can also train your internal engineers to handle these independently — capability transfer is a core DCIT differentiator, not an upsell.
Absolutely. AI Systems Integrator is commonly combined with API & Integration Development (to connect AI to existing systems), Custom Software Development (to build the product layer around the AI pipeline), and Technical Consulting (for CTO-level architecture reviews before committing to a full build).
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Let's Discuss Your AI Integration
Tell us about your AI initiative and we'll explain how we can take it to production.