Forward Deployed Engineers · Gen AI Delivery
Production Gen AI, delivered. Agentic systems and RAG, grounded in your data.
Senior engineers embedded with your team to design, build, and operate Gen AI systems on your data. Hybrid retrieval, tool-using agents, multi-step graphs, eval harnesses, and tracing. Built so the system works on a Wednesday afternoon, not just in the demo.
Built on
We also work across AWS Bedrock, Azure OpenAI, Google Vertex, open weights, and the broader data stack (Snowflake, Databricks).
What we build
Real Gen AI systems, not demos.
We focus on outcomes you can put in front of customers and auditors. Every system we ship has evals, guardrails, and a clear cost picture.
Task agents that earn their keep
Single-purpose agents with bounded tool sets, structured outputs (JSON schema or Pydantic), retry policies, and human approval steps where they matter. ReAct or planner-executor patterns, picked based on the task and how forgiving it is.
Agentic systems and multi-step flows
Plan, route, execute, verify loops with explicit state. We use LangGraph or hand-rolled graph executors when control flow matters more than chains. Idempotent steps, bounded retries, and timeout budgets so the system fails predictably.
RAG that holds up in production
Chunking tuned to your documents (semantic, recursive, or layout aware). Hybrid retrieval (BM25 plus dense), reranking with cross-encoders, and source attribution on every answer. Tested with golden sets and offline regressions before any prompt change ships.
Evals, guardrails, and tracing
Offline evals against curated sets, online evals from production traces, and LLM-as-judge with calibration. PII redaction, jailbreak filters, policy checks. OpenTelemetry traces across model calls, tools, and retrieval so debugging is not guesswork.
Our stack
Modern Gen AI tooling, picked for the problem.
Vendor neutral. We pick what fits your data, your scale, and your constraints.
Models & APIs
- Claude (Sonnet, Haiku, Opus) for reasoning and tool use
- OpenAI GPT and Codex for code and long context
- Open weights (Llama 3, Mistral, Qwen) for cost or on-prem
- Voyage, Cohere, OpenAI for embeddings; bge-reranker for rerank
Agent and orchestration
- LangGraph for stateful graphs and human-in-the-loop
- Anthropic and OpenAI native tool use SDKs
- Structured outputs via JSON schema or Pydantic
- Workflow runners (Temporal, Inngest) for durable execution
Retrieval and data
- pgvector, Pinecone, Weaviate, Qdrant, picked on scale and ops fit
- Hybrid BM25 + dense, cross-encoder reranking
- Document parsing (Unstructured, LlamaParse, Azure DI)
- Snowflake Cortex, Databricks Vector Search for in-platform RAG
Production
- Eval suites: Ragas, Promptfoo, Braintrust, custom LLM-as-judge
- Tracing: Langfuse, LangSmith, Arize, OpenTelemetry
- Guardrails: NeMo Guardrails, Llama Guard, prompt injection filters
- Cost, p95 latency, rate-limit, and token budget controls
Where this works
Use cases we deliver against.
- Internal copilots for support, sales, and operations
- Document and contract intelligence with RAG over your corpus
- Customer facing AI features inside your product
- Agentic workflows for back office and data ops
- Code agents that refactor, migrate, or modernize large repos
- Voice and chat assistants grounded in your knowledge base
How we build
Production rules of the road.
Gen AI fails when teams skip the boring parts. These are the rules we hold every project to.
Evals come first, prompts come second
We build the offline eval set and metrics before the first prompt: faithfulness, groundedness, citation accuracy, tool-call correctness. Every prompt or model change runs against the set. If we cannot measure it, we will not ship it.
Retrieval over generation, every time we can
Hallucination is a retrieval problem in disguise. We invest in chunking, hybrid search, reranking, and source attribution. Structured outputs with JSON schema for anything that touches a database, a UI, or another system.
Narrow first, broad later
One agent that does one task well beats a wide agent that does five things poorly. We scope tight, measure, then expand scope only when the evals support it. Failure modes get easier to debug at small scope.
Human in the loop where the stakes warrant it
Anything customer facing, financial, or regulated gets an approval step, a confidence threshold, or both. Reviewer UX is part of the system, not an afterthought. We log every override so the model learns from the corrections.
Cost and latency are features
We set token and latency budgets per request type up front. Route to small models where they are good enough. Cache. Stream. Truncate context aggressively. Track cost per resolved task, not just cost per call.
Observability or it never happened
OpenTelemetry traces across the full call graph: model inputs, retrieved chunks, tool calls, structured outputs, eval scores. When a user says the bot got it wrong, we can pull the exact trace and reproduce it.
How we engage
From first call to first commit, fast.
- 1
Scope
One short call to align on the outcome, constraints, data, and success metrics.
- 2
Match
We bring forward one or two senior engineers who fit the stack and the problem.
- 3
Build
Working prototype in weeks. Tight loop of eval, iterate, and ship. Weekly progress.
- 4
Productionize
Guardrails, observability, cost controls, handover. Your team owns the system.
Let us help you move faster
Need talent or a delivery partner? Start a conversation.
Tell us what you are building or hiring for. We will respond with a clear next step.