AI Engineering Services
Hire an AI Engineer
Who Actually Ships.
Most AI engineers prototype. Few ship. The gap between a working notebook and a production-grade AI system that handles real users, real data, and real scale is where most AI projects die. Guldstreet’s AI engineering practice was built to close that gap. We bring together machine learning expertise, software engineering rigour, and twenty years of delivery discipline to build AI systems that work in production — not just in demos. Whether you need an LLM-powered product built from scratch, a machine learning pipeline wired into your existing stack, or an AI automation layer that eliminates manual work your team should not be doing, we deliver end-to-end.
What We Build
From internal productivity tools to customer-facing AI products — built to production standards, not proof-of-concept prototypes.
Internal AI Tools & Copilots
Multiply what your team can produce
- Enterprise knowledge bases and internal document Q&A systems
- AI writing assistants and content generation tools
- Intelligent search across internal data sources and repositories
- Meeting summarisation, action tracking, and decision-support tools
Customer-Facing AI Products
AI your customers actually use
- Conversational AI and chatbots with domain-specific knowledge
- Personalisation engines that adapt to individual user behaviour
- AI-powered recommendation systems for products, content, and services
- Intelligent onboarding flows and guided user experiences
Operational AI & Automation
Automate the work that scales badly
- Intelligent document processing: extraction, classification, and routing
- Predictive models for demand, churn, credit risk, and equipment failure
- Automated QA and compliance checking pipelines
- AI-powered data enrichment and entity resolution at scale
AI-Native SaaS & API Products
AI as the product, not the add-on
- Full-stack AI application development (Python, FastAPI, React)
- AI API design and multi-provider orchestration layers
- Agentic AI systems with tool use, memory, and autonomous decision-making
- Retrieval-augmented generation systems with production-grade vector stores
Who This Is For
Three types of client, one consistent outcome: AI that works in production.
Startup Founders with an AI Product Vision
You have a clear AI product idea and need an engineering team that can take it from whiteboard to live product without burning your runway on exploratory work that never ships. You need people who have built before, who can make sound architecture decisions under resource constraints, and who treat your deadline as real.
Scope your AI build →Non-Technical Founders & Business Leaders
You understand the business problem clearly but need a senior technical partner who can translate it into sound architecture decisions and build something you can maintain, scale, and explain to investors. You do not want to manage freelancers or navigate conflicting technical opinions — you want a team that owns the outcome.
Get a free scoping call →CTOs & Engineering Leaders Extending a Team
You need senior AI engineering capacity on a specific initiative, for a defined period, with clear deliverables — without the cost, time, and risk of a full-time hire who may not exist in this labour market. You want someone who integrates with your team, follows your standards, and ships.
Discuss an embedded engagement →Engagement Models
Three ways to work with us, depending on what you need to build and how quickly.
Project-Based Build
Defined scope · Fixed fee · Clear deliverablesRight for greenfield AI product builds or defined capability additions to an existing system. You know what you want to ship. We define the architecture, build it, and hand over a working production system.
Typical deliverables:
- Technical architecture and system design document
- Production AI system, deployed and monitored
- Data pipeline and integration layer
- Model performance documentation and handover
Embedded Engineering Retainer
Monthly engagement · Dedicated capacity · FlexibleMonthly retainer scoped at the start of each quarter. No open-ended billing, no seat-warming. You get senior AI engineering capacity applied to the work that matters most.
Right for organisations building AI capability continuously and needing consistent senior expertise without the overhead and risk of a full-time hire.
Ongoing support includes:
- Active AI engineering on your highest-priority build
- Architecture review and technical decision-making
- Code review and engineering standards
- Integration with your existing team and stack
Architecture & Technical Leadership
Fractional CTO-equivalent · Defined hours · Senior accessScoped by the hour or day. Right when you have internal engineers but need senior AI architecture oversight to avoid the expensive mistakes that show up at scale.
For organisations with internal development capacity that need a senior AI engineering partner to own the architecture, make the key technical decisions, and ensure the team is building the right thing in the right way.
Includes:
- AI architecture review and design approval
- Technical decision-making support and trade-off analysis
- Build vs. buy vs. fine-tune recommendations
- Code review and engineering quality standards
What Every Engagement Delivers
Concrete outputs at every stage — from initial architecture through to a production system with handover documentation your team can actually use.
Technical Architecture & Design Document
The full system design before a line of production code is written — model selection rationale, data architecture, integration design, deployment strategy, security requirements, and the observability layer that tells you when something is wrong before your users do.
Production AI System, Deployed
A live, working AI system — not a prototype, not a proof of concept — with monitoring, logging, error handling, and the operational documentation your team needs to manage it without us in the room.
Data Pipeline & Infrastructure
The data engineering layer that feeds your AI system — ingestion, processing, transformation, storage, and the quality controls that determine whether your model’s outputs can be trusted. AI is only as good as the data it runs on.
Integration & API Layer
The connectors, APIs, and webhooks that wire your AI system into the tools, workflows, and products your team and customers already use — so it fits the way people actually work rather than requiring a behaviour change to be adopted.
Model Performance Documentation & Handover
Baseline accuracy metrics, known limitations, failure modes, monitoring thresholds, and a retraining guide — everything your team needs to manage and improve the system after we have handed it over.
Why Hire Guldstreet Instead of a Freelancer or AI Agency
The difference is not in the model. It is in the delivery.
We Build for Production, Not Demos
Every architecture decision is made with production requirements in mind: latency, cost, reliability, security, and the operational complexity of maintaining it six months after launch. We have seen what happens when those decisions are deferred — it is expensive to fix later.
End-to-End Ownership
We take responsibility for the full delivery lifecycle — from architecture through engineering, testing, deployment, and knowledge transfer. You do not coordinate between a strategist, a data engineer, an ML engineer, and a DevOps contractor. You work with one team that owns the outcome.
20 Years of Delivery Discipline
We bring programme management rigour to AI engineering: scoped deliverables, defined timelines, regular check-ins, and no surprises at go-live. We have delivered complex technical programmes at IBM, Google, eBay, and across 50 countries. We know how to ship.
We Solve the Business Problem
The AI system we build is designed around the outcome you are trying to achieve — cost reduction, revenue growth, risk reduction, or productivity gain. Not around what is technically interesting to build. The goal is always a business result, not a technical artefact.
Areas of Expertise
LLM Engineering
- Prompt Architecture & Optimisation
- LangChain & LlamaIndex
- Agentic AI & Tool Use
- OpenAI, Anthropic, Gemini APIs
- Multi-Model Orchestration
- Structured Output & Function Calling
RAG & Knowledge Systems
- Vector Database Design (Pinecone, Weaviate, pgvector)
- Embedding Pipeline Engineering
- Hybrid Search Architecture
- Document Parsing & Chunking Strategy
- Knowledge Graph Integration
- Context Window Management
Machine Learning Engineering
- Fine-Tuning & PEFT / LoRA
- Classification & Regression Models
- Recommendation Systems
- Time-Series Forecasting
- Anomaly Detection
- Computer Vision (CNNs, YOLO, SAM)
MLOps & Production AI
- AWS SageMaker, Azure ML, Vertex AI
- Model Monitoring & Drift Detection
- CI/CD for ML Pipelines
- FastAPI & AI Microservice Architecture
- Docker, Kubernetes for AI Workloads
- Cost Optimisation & Latency Tuning
Let’s Jump On a Free AI Engineering Scoping & Requirements Audit Call
30 minutes · Free · No strings attached
What to expect:
- You describe the challenge you’re facing and the outcome you’re aiming for
- We’ll ask clarifying questions to understand the full context
- We’ll outline how we’d approach it — scope, timeline, and what’s realistic
- You’ll get honest advice — even if it’s “you don’t need a consulting firm for this”