Services — 7 ways I can help you ship

PICK A PROBLEM.
I'LL SHIP THE SYSTEM.

Every service below is a fixed, well-bounded engagement — you see the system blueprint before we start, you get weekly updates while we build, and you keep full documentation when we hand off. No retainers, no vague scope.

1–30
DAYS PER ENGAGEMENT
WEEKLY
UPDATES + SLACK ACCESS
1 ROUND
OF POST-LAUNCH REVISIONS
FULL
HANDOFF DOCUMENTATION
01AI Integration02AI Agents & Automation03Fine-Tuning & Self-Hosted Models04LLMOps & Evaluation05Full-Stack Development06DevOps & Infrastructure07Data Pipelines
Blueprint of a retrieval-augmented generation pipeline from documents to a cited answer
FIG.01The pipeline I build: your documents → hybrid retrieval → grounded, cited answers
SERVICE 01 / 07

AI Integration

LLM pipelines, RAG systems, and intelligent features embedded directly into your product. From proof-of-concept to production-grade infrastructure.

What's included
✓
Custom RAG pipeline with vector search including chunking, embedding caching, async ingestion, and hybrid BM25 + vector retrieval
✓
LLM selection, prompt engineering & evaluation
✓
Streaming API with cost & latency optimisation
✓
Observability: tracing, logging, evals dashboard
✓
Handoff documentation & team walkthrough
LangChainOpenAIClaudeQdrantPineconeFastAPIAWS
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1 – 30 days
Typical engagement: 1–8 weeks depending on scope. Includes one round of revisions post-launch.
Blueprint of an agent loop — plan, act through tools, observe — with a human approval gate
FIG.02Bounded agent loops with real tools — and a human gate on every risky action
SERVICE 02 / 07

AI Agents & Automation

Autonomous, multi-step agents that take real actions — tool-calling, stateful workflows, and MCP integrations into your existing systems, with humans in the loop where it matters.

What's included
✓
Agent design with LangGraph — planning, tool-calling, memory & bounded loops
✓
MCP servers connecting agents to your databases, APIs, files & ticketing
✓
Human-in-the-loop approvals for high-risk actions
✓
Retries, guardrails & graceful failure handling
✓
Deployment, monitoring & handoff documentation
LangGraphLangChainMCPOpenAIClaudeFastAPIPostgreSQL
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1 – 30 days
Great fit for support, ops, and back-office automation. Includes one round of revisions post-launch.
Blueprint of an open-weight model with LoRA adapters distilled and deployed inside a private VPC
FIG.03Your domain, your weights, your VPC — tuned models that answer to nobody else
SERVICE 03 / 07

Fine-Tuning & Self-Hosted Models

Own your models — fine-tune or distill open-weight LLMs for your domain and run them in your own VPC or on-prem, for lower cost, data privacy, and compliance.

What's included
✓
Dataset curation, fine-tuning & LoRA / QLoRA adapters
✓
Distillation to smaller, faster task-specific models
✓
Self-hosted inference (vLLM / Ollama) with GPU autoscaling
✓
Benchmarking vs. hosted APIs — quality, latency & cost
✓
Private VPC / on-prem deployment & handoff
PyTorchHugging FaceLoRAvLLMOllamaDockerAWS
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1 – 30 days
Best when API cost, latency, or data-residency rules push you off hosted models. Includes one round of revisions.
Blueprint of an evaluation dashboard with pass rates, traces, guardrails and cost tracking
FIG.04Evals, traces, and guardrails — production AI you can actually measure
SERVICE 04 / 07

LLMOps & Evaluation

Make the AI you already shipped trustworthy in production — evaluation, tracing, guardrails, and cost/latency control so quality stops being a guess.

What's included
✓
Automated eval suites & regression tracking (LLM-as-judge + datasets)
✓
Tracing & observability across chains, agents and tools
✓
Guardrails: PII redaction, output validation, spend & rate limits
✓
Prompt-injection defence & red-team testing
✓
Cost & latency optimisation with a live metrics dashboard
LangSmithLangChainClaudeOpenAIQdrantRedisGrafana
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1 – 30 days
Ideal once AI is live and you need reliability, safety & cost control. Includes one round of revisions.
Blueprint of a full application stack — interface, API layer, services and database — with a CI/CD line
FIG.05Interface to database, one coherent system — shipped through CI/CD from day one
SERVICE 05 / 07

Full-Stack Development

End-to-end web applications — schema design, API architecture, and polished interfaces built to hold up under real traffic and real users.

What's included
✓
Frontend development with Angular, React, Next.js, Tailwind UI or custom designs
✓
Backend development with Python (Django/FastAPI), Java (Spring Boot), or Node.js
✓
Database design, migrations & query optimisation
✓
CI/CD pipeline with automated testing
✓
Performance audit, Monitoring & Core Web Vitals pass
Next.jsAngularReactPythonJavaTypeScriptMySQLMongoDBJenkins, GitHub Actions or GitLab CI
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1 – 30 days
Blueprint of a Kubernetes cluster behind a load balancer with a CI/CD pipeline and monitoring pulse
FIG.06Clusters, pipelines, and monitoring — infrastructure that pages you before your users do
SERVICE 06 / 07

DevOps & Infrastructure

CI/CD pipelines, containerisation, and cloud deployments. Infrastructure that scales silently and fails gracefully.

What's included
✓
Docker + Kubernetes cluster setup
✓
GitHub Actions or GitLab CI pipeline
✓
Cloud infrastructure on AWS or GCP (IaC)
✓
Monitoring, alerting & on-call runbooks
✓
Security audit & secrets management
DockerKubernetesTerraformGitHub ActionsPrometheusJenkinsAWSGCP
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1 – 30 days
Blueprint of a data pipeline — sources streaming through transforms into a warehouse, vector store and dashboard
FIG.07From raw events to live dashboards — streams, transforms, and stores that stay in sync
SERVICE 07 / 07

Data Pipelines

ETL workflows, vector databases, and real-time analytics. Raw, messy data transformed into fast, reliable decisions.

What's included
✓
ETL / ELT pipeline design & implementation
✓
Vector store setup & embedding strategy
✓
Real-time streaming with Kafka or Pub/Sub
✓
Analytics dashboard (Metabase or custom)
✓
Data quality monitoring & alerting
PythonAirflowKafkaBigQueryPinecone
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1 – 30 days