# Solution architecture and delivery

> I turn a business problem into an AI solution people sign off on, and then ship it.

Azure and AWS reference architectures, proofs of concept, executive briefings, offerings and pilot plans, then delivery against real constraints: SSO, audit trails, legacy estates and measurable outcomes.

- Core skills: Azure (AKS, ML, OpenAI, Databricks), AWS (Bedrock, SageMaker, Lambda), Legacy modernization, AI governance and standards
- Domains: Telecom, Banking and financial services, Quality engineering, Legacy modernization, Conversational and voice AI, Research and public sector
- URL: https://farhanbaluch.com/capabilities/solution-architecture

## Case studies

- [GenAI solution architecture and pre-sales](https://farhanbaluch.com/work/genai-solution-architecture.md) (Oct 2024 – 2025, Enterprise banking and telecom clients): 3, enterprise client wins, from 10+ proofs of concept. Most enterprise AI work stalls between "this is interesting" and "this is approved". Reusable blueprints for RAG, agents and guardrails let each proof of concept start from a sound architecture, and the briefings turned leadership questions into pilots with a clear measure of success.
- [Risk-based test design with agents](https://farhanbaluch.com/work/risk-based-test-design.md) (Sep 2025 – Oct 2026, Internal R&D and client pitch): 9 of 9, injected bugs caught, against 1 of 9 for the standard set. AI assistants write tests faster than anyone can review them, and more tests isn't better coverage. This experiment showed that structured, risk-based agent workflows find bugs the default approach misses, and it became the basis for an agentic quality-intelligence offering.
- [Realtime voice agent for banking onboarding](https://farhanbaluch.com/work/banking-voice-onboarding.md) (Dec 2024, Enterprise bank, proof of concept): Designed and built the agent. Voice is where agents meet customers with no screen to fall back on. A realtime speech-to-speech agent with a confidence-based handoff keeps the customer moving without letting the model guess on a regulated process.
- [Agentic legacy modernization and code intelligence](https://farhanbaluch.com/work/legacy-modernization.md) (2025 – 2026, Enterprise bank): 70%, shorter modernization timeline. Spec-driven agents transform the code and a multimodal agent verifies migrated reports, while a GraphRAG platform gives engineers and agents a queryable map of the estate.
- [Estimation platform for an enterprise bank](https://farhanbaluch.com/work/estimation-platform.md) (2025, Enterprise bank): 80%, less manual estimation effort. Agents research and draft the estimate, a regression model anchors the number, retrieval over past projects supplies citations, and an expert approves. The AI does the slow part and a person keeps the decision.
- [Applied AI prototypes, from crops to small models](https://farhanbaluch.com/work/applied-ai-prototypes.md) (2023 – present, Public sector, small businesses and independent work): 25% → 75%, exact-match extraction accuracy after QLoRA fine-tuning of Llama 3.2. The models underneath agents: vision, retrieval and fine-tuning. Knowing when a small fine-tuned model beats a large prompted one, and how to measure it, is what lets me choose the right tool on client work.

## Evidence from the log

- 2026-10-08: **Pitched an agentic quality-intelligence offering.** Turned the risk-based test design results into a service offering, with a demo, a pilot plan and the evidence from the bug-injection study. AI angle: Packages agent-driven test design, AI-generated tests and coverage data into something a client can buy, backed by measured results rather than claims about AI. [Solution architecture and delivery; AI evaluation and quality] (https://farhanbaluch.com/work/risk-based-test-design.md)
- 2026-03-02: **Began adapting an agentic test-architecture framework for an enterprise.** AI assistants were writing tests fast, but many were redundant or asserted nothing. Started extending a test-architecture framework with connectors for the client's stacks. AI angle: Puts structure around AI-generated tests (risk registers, traceability, quality gates), so agents produce tests that matter rather than just more tests. [Solution architecture and delivery; AI evaluation and quality] (https://farhanbaluch.com/work/risk-based-test-design.md)
- 2025-12-08: **Finished the report migration ahead of plan.** Crystal Reports moved to JasperReports with multimodal agents that compare old and new report output. The modernization ran 70% faster than planned. AI angle: A vision-capable agent checks the migrated report against the original, which is what made automated migration safe enough to trust. [Agent architecture and orchestration; Solution architecture and delivery] (https://farhanbaluch.com/work/legacy-modernization.md)
- 2025-10-14: **Ran executive briefings on agentic AI.** Presented reference architectures and live demos to client leadership, and turned their questions into scoped proofs of concept. AI angle: Translates agentic AI into decisions leadership can make: what to pilot, what it needs, and how success will be measured. [Solution architecture and delivery] (https://farhanbaluch.com/work/genai-solution-architecture.md)
- 2025-08-18: **Cut manual estimation effort by 80%.** The estimation workflow went live with SME review and an immutable audit trail. Estimators now review and adjust a cited draft instead of starting from a blank sheet. AI angle: Agents do the research and drafting, and a person keeps the decision. Human-in-the-loop plus an audit trail is what let a bank put it into production. [Agent architecture and orchestration; Solution architecture and delivery] (https://farhanbaluch.com/work/estimation-platform.md)
- 2025-01-27: **Drew Azure and AWS architectures for GenAI proofs of concept.** Reference designs for RAG, agent hosting and guardrails that pre-sales could adapt per client. They backed 10+ proofs of concept and 3 client wins. AI angle: Reusable blueprints for RAG, agents and guardrails, so each proof of concept starts from a sound architecture instead of a blank page. [Solution architecture and delivery; Knowledge, retrieval and context] (https://farhanbaluch.com/work/genai-solution-architecture.md)
- 2024-12-09: **Built a realtime voice agent for banking onboarding.** A LiveKit and OpenAI Realtime voice agent that walks new customers through account opening, with handoff to a human when it isn't sure. AI angle: Speech-to-speech agents with a confidence-based handoff. Building one taught me where voice agents fail, which later shaped how I test them. [Agent architecture and orchestration; Solution architecture and delivery] (https://farhanbaluch.com/work/banking-voice-onboarding.md)
- 2024-10-21: **Joined mobileLIVE (now ML Arteka) as an AI Engineer.** First weeks on proofs of concept for enterprise banking clients, from RAG assistants to agent workflows. AI angle: Moved from research into building LLM systems for clients, starting with RAG and agent proofs of concept. [Solution architecture and delivery; Knowledge, retrieval and context] (https://farhanbaluch.com/work/genai-solution-architecture.md)
- 2024-01-15: **Took small-business AI use cases from problem to prototype.** At the Vector Institute, helped small and mid-sized businesses define an AI use case and reach a working prototype in three months. AI angle: Early practice in what solution work really is. Most of it is scoping the problem so a model can actually help. [Solution architecture and delivery; Applied ML and research] (https://farhanbaluch.com/work/applied-ai-prototypes.md)