Enterprise AI, RAG Search & Agent Workflows
Grounded retrieval-augmented generation (RAG) over private company documents, tool-calling LLM workflows, evaluation harnesses, and private cloud deployment.
- Operations and knowledge-heavy teams spending hours searching SOPs, contracts, or support tickets
- CTOs who want practical LLM features in production with measurable accuracy—not brittle prompt demos
- Regulated EU/UK businesses requiring VPC-isolated inference (AWS Bedrock, Azure OpenAI, GCP Vertex)
Technical bottlenecks we resolve
Concrete operational and architectural issues we eliminate for engineering teams.
Chatbots hallucinating answers or leaking restricted internal documents across roles
Permission-aware hybrid retrieval (vector + keyword) enforcing source citations and confidence thresholds.
No objective way to tell whether a prompt or model change improved or broke accuracy
Automated evaluation datasets scoring faithfulness, citation precision, and latency before deployment.
Data residency concerns blocking public SaaS AI adoption
Deploy inference inside your own AWS EU-West (Dublin) or GCP VPC with zero model training on your data.
Grounded Enterprise RAG & Tool-Calling Pipeline with Evaluation Guardrails
Document Parsing & Chunking
Layout-aware chunking, metadata tagging, and ACL permission mapping.
Hybrid Vector + Keyword
Semantic embeddings combined with exact lexical match and reranking.
Guarded Workflow
Typed tool calls, input/output guardrails, and source citation checks.
Observability & Evals
Regression accuracy scoring, token cost tracking, and latency alerts.
Layered Input & Output Guardrails
Prompt boundary defenses, PII scrubbing, structured JSON outputs, and citation grounding checks.
EU / Private Cloud Residency
Run via AWS Bedrock (Dublin eu-west-1), Vertex AI, or private open-weight models in your VPC.
Human-in-the-Loop Controls
High-impact actions pause for explicit user confirmation with full audit logging.
Measurable Eval Harness
Every prompt or chunking change is benchmarked against a repeatable golden test dataset.
Start with one bounded workflow before any ongoing commitment
Clear deliverables, client prerequisites, exclusions, and acceptance criteria so expectations match on both sides.
- Working RAG or agent workflow wired to your sample document set on staging
- Automated evaluation script scoring retrieval accuracy across agreed test questions
- Typed FastAPI streaming service with source citations and token telemetry
- Docker container and Terraform/Compose deployment config for your cloud
One grounded RAG search or document extraction workflow evaluated against 25–50 representative test queries.
Sample non-production document corpus, golden Q&A examples, and cloud account or API credentials.
Foundational LLM pre-training from scratch, OCR of illegible handwritten scans, and open-ended autonomous trading/medical diagnosis.
Deployed staging API + UI meeting mutually agreed retrieval precision and latency targets on the test set. Backed by a 10-business-day defect remediation window. 100% of IP assigns upon pilot invoice settlement.
Multi-Clinic Telehealth & Appointment Orchestration Suite
Completed Client Engagement (Client anonymized under mutual NDA)
Client Problem
A growing clinic group struggled with double-booked practitioner slots across branches and fragmented video links sent manually over SMS.
What We Delivered
- Transactional PostgreSQL appointment scheduler preventing concurrent double-bookings
- Kotlin Multiplatform shared patient booking & consultation prep module (iOS & Android)
- Browser & mobile WebRTC video session rooms with automated SMS/email reminders
Measured Results & Basis
Measured across shared KMP networking, models, and booking state machines vs. native UI layers.
Tracked via synthetic uptime checks on booking and consultation signaling endpoints over 6 months.
Common technical & commercial questions
Key implementation, security, and pilot sprint answers for your engineering team.
Can you completely eliminate LLM hallucinations or prompt injection?↓
No LLM system can claim 0% risk across arbitrary inputs. We substantially reduce risk through strict schema validation, permission-scoped retrieval, mandatory source citations, input/output classifiers, and human-in-the-loop gates for state-changing actions.
Can our data stay inside EU / Irish cloud regions?↓
Yes. We routinely deploy against AWS Bedrock in eu-west-1 (Dublin), Azure OpenAI EU regions, or self-hosted open-weight models inside your private Kubernetes cluster.
Scope a Applied AI & RAG sprint with Taimoor Hassan
Your selected service carries automatically into our discovery scheduler or enquiry form.