Bring your
experience.
Build what
comes next.

• Map the current workflow, decision owners, exception paths and data access; establish a measurable baseline.
• Compare deterministic automation, classical ML and LLM approaches; write explicit stop/go criteria.
• Specify acceptance tests, exclusions, dependency risks, change requests and a phased commercial proposal.

Value at work: Turn ambiguous requests into a delivery commitment that engineering, operations and the sponsor can evaluate.

• Benchmark a baseline, small model and stronger model on the same tasks; route by quality, cost and latency.
• Design context budgets, structured outputs, session memory, compaction and expiration; separate user and tenant state.
• Choose retrieval, fine-tuning or a non-LLM model using error analysis; treat LoRA and private inference as elective investigations.

Value at work: Explain which model belongs in a workflow and what evidence would justify changing it.

• Build hybrid retrieval and reranking; compare chunking, metadata filtering, query rewriting and citations.
• Enforce document access controls before retrieval; manage freshness, deletes, lineage and tenant boundaries.
• Add OCR/table extraction and schema contracts; evaluate retrieval relevance separately from answer quality.

Value at work: Make enterprise knowledge useful without exposing restricted documents or relying on stale evidence.

• Model explicit states, checkpoints, timeout budgets, cancellation and resumable jobs.
• Use idempotency keys, retry policies, compensating actions and approval gates around external writes.
• Compare a single agent, a deterministic workflow and multi-agent delegation; add delegation only when evaluation supports it.

Value at work: Recover a customer workflow after a failure without duplicating actions or losing ownership.

• Design typed tool contracts, schema validation and versioning for CRM, ticketing and legacy APIs.
• Separate user delegation from service identity; validate audience and scopes, minimise privileges and avoid token passthrough.
• Handle pagination, throttling, webhooks and tenant isolation; constrain outbound access and test SSRF paths.

Value at work: Connect AI to enterprise systems with permissions that can be inspected and defended.

• Create representative tasks, labelled holdouts, hard negatives and failure taxonomies; prevent evaluation leakage.
• Combine deterministic checks, calibrated model graders and human review; score outcomes and tool trajectories.
• Version prompts, datasets and models; run regression suites, shadow tests and controlled releases with rollback criteria.

Value at work: Replace subjective demo approval with evidence that a change improves the intended workflow.

• Threat-model indirect prompt injection, data exfiltration, tool misuse, poisoned content and unbounded spend.
• Apply output validation, sandboxing, secret isolation, redaction and human approval to high-impact actions.
• Document data flows, retention, residency choices, owners and incident evidence for client security review.

Value at work: Translate a security objection into an explicit control, owner and verification step.

• Select serverless functions, durable workers or containers from workload duration, networking and latency requirements.
• Implement infrastructure as code, environment separation, secret rotation, identity controls and staged rollout.
• Evaluate managed APIs against private inference; benchmark batching, quantization and GPU utilization only in an elective lab.

Value at work: Choose an architecture that fits the client's operating constraints and budget.

• Trace retrieval, model calls and tools with correlation IDs; log useful evidence without leaking customer data.
• Define user-visible SLOs for latency, availability and task completion; use error budgets to decide release pace.
• Measure cost per successful task, token usage, retry amplification and queue lag; add caching, quotas and circuit breakers.

Value at work: Operate AI as a service with measurable reliability and an explainable cost envelope.

• Separate common product capabilities from tenant-specific integrations, rules and deployment configuration.
• Define connector contracts, migration paths, feature flags, release compatibility and customer onboarding templates.
• Turn field discoveries into a product backlog; quantify recurring support effort and document the next-client deployment.

Value at work: Convert one successful engagement into an approach the next customer can adopt with less bespoke work.

• Run architecture reviews with engineering, security, operations and a business sponsor in the room.
• Explain build-versus-buy decisions, capacity value, delivery risk and adoption metrics without overstating ROI.
• Handle a failed live demo, scope pressure and disagreement; produce a handover that an operations team can use.

Value at work: Use existing industry experience to lead decisions across technical and business boundaries.

• Own one domain problem with two integrations, access controls, bounded agent actions and an evaluation suite.
• Run a simulated incident and a release regression; show recovery, rollback and the cost of success.
• Present measured results, limitations and a 90-day adoption plan; package an individual portfolio contribution.

Value at work: Show a reviewer what you built, why it works, where it fails and how a customer would operate it.