Agentic Harnesses
Task-specific scaffolding that makes a model dependable at one job. The harness — not the model — is usually what's broken when an agent loops, hallucinates a tool call, or silently does the wrong thing.
Tool calling
Designing tool schemas, argument validation, and error-recovery loops so the model picks the right call and recovers gracefully when it doesn't.
Structured outputs
Constraining generations to JSON / schema-conformant shapes that downstream code can trust, with graceful fallback when the model goes off-format.
Agentic workflows
Multi-step plan → act → observe loops with the right stopping conditions, budgets, and guardrails so agents finish the task instead of wandering.
An agent that completes its task reliably, fails loudly instead of silently, and is cheap to extend with new tools.
Retrieval & Knowledge
Connecting models to your actual documents and data — so answers are grounded in your knowledge base instead of the model's training data. Covers ingestion, retrieval, and summarization end to end.
Retrieval-augmented generation
Chunking, embedding, and retrieval strategy tuned to your corpus — plus the reranking and prompt assembly that decide whether RAG actually helps.
Vector databases
Choosing, indexing, and operating the vector store — embedding refresh, metadata filtering, and keeping recall high as the corpus grows.
Google Drive & MCP sources
Wiring live document sources into the model via MCP servers, so the knowledge base stays in sync with where your team actually works.
Document summarization
Map-reduce and hierarchical summarization for long documents, with structured extraction and optional human-labeled annotation for quality.
Grounded, citeable answers from your own data — with a retrieval layer you can measure and tune, not a black box.
Streaming & Performance
Latency is a product feature. I make LLM responses feel instant through streaming architecture and targeted optimization of the moments users actually perceive.
Time-to-first-token
Cutting the gap between request and first visible token — the single biggest driver of perceived speed — through prompt caching, routing, and warm paths.
Streaming architecture
Token-by-token streaming through the full stack — server, transport, and UI — including partial tool-call and structured-output streaming.
Response-speed optimization
End-to-end latency budgeting: finding where the seconds go across retrieval, generation, and post-processing, then closing the gaps that matter.
Responses that start instantly and stream smoothly — measured in real TTFT and p95 latency, not vibes.
Model Proxying & Infra
The platform layer every LLM feature should sit behind: one gateway that handles routing, cost, observability, and safety — so individual features don't each reinvent it.
In-house metrics
First-class observability for every call — tokens, cost, latency, cache hits, error rates — feeding dashboards and alerts you actually own.
Model switching & cost optimization
Routing each request to the cheapest model that can do the job, with fallback chains and A/B routing — often the fastest way to cut spend without losing quality.
PII redaction
Detecting and stripping sensitive data before it reaches a provider, with reversible tokenization where the workflow needs the original back.
One proxy that makes every downstream feature cheaper, observable, swappable, and safe by default.
Conversational Products
Where the previous five layers come together into something users talk to. Support chatbots are the showcase: retrieval, harness, streaming, and proxy in one product, judged on real outcomes.
Response generation
Grounded, on-brand answers that cite sources, know when to escalate to a human, and stay within policy — built on the retrieval and harness layers.
Support-speed optimization
Cutting time-to-resolution and first-response time — combining streaming, caching, and smart routing so customers feel helped immediately.
A support assistant that resolves more tickets faster — measured on deflection and resolution time, not message count.
Evals & Reliability
The discipline that holds all the above accountable. Without evals, every change to a prompt or model is a guess. I build the measurement that turns "seems better" into a number.
Eval development
Task-specific eval suites — golden sets, rubrics, and graders — so you can compare prompts and models on the criteria that matter to your product.
LLM-as-judge
Automated graders calibrated against human labels, so evaluation scales beyond what you can hand-score while staying trustworthy.
Data annotation
Building the labeled datasets that everything else depends on — annotation guidelines, inter-rater agreement, and feedback loops from production.
Confidence to ship changes — every model or prompt swap backed by a number that moves the right direction.
Have an LLM feature that needs to be production-grade?
Whether it's an agent that won't stay on task, a RAG pipeline that hallucinates, or a model bill that won't stop growing — I can help.
Get in touch →