1
Discovery sprint
Weeks 1–2
We map the problem space, run technical spikes, and produce a build plan with cost, risk and a real eval set.
Make AI a line in your roadmap, not a side project.
Many AI projects stall at the pilot stage. We build retrieval, evaluation and analytics systems that deliver value from the first release, and we stay accountable for the eighteen months after.
What our AI work covers.
AI & Data Solutions starts with a loosely defined AI goal (“do something with our docs,” “replace this manual review,” “surface insight from these tickets”) and ends with code in production, evals in CI and a team that knows how to run it.
We focus on the foundational work many teams skip: retrieval that finds the right sources, evals that catch regressions, and latency budgets that hold under traffic.
Where it fits
Series B/C SaaS
Your product team needs AI features that work reliably in front of customers. We build the retrieval, the guardrails and the eval harness.
Mid-market enterprise
You’ve spent twelve months on a copilot that nobody uses. We help you triage what to keep, what to rebuild and what to retire.
Regulated industries
Healthcare, finance, government: where every output needs a citation, an audit trail and a fallback. We’ve built for HIPAA, SOC 2 and FedRAMP-aware contexts.
Founders shipping v1
You need a working product, not a research lab. We reach a deployable product in eight to twelve weeks with a real eval set.
What we deliver.
Each area below is a scoped deliverable. We plan it, build it and stay accountable for the result.
RAG & retrieval systems
Hybrid retrieval (BM25 + vector + reranker), chunking strategies that respect document semantics, citation tracking. Experience has taught us that many apparent model failures are really retrieval failures.
Document intelligence
Extraction pipelines for contracts, claims, SOWs, clinical notes and regulatory filings. Layout-aware models, table extraction, structured output validation.
Agentic workflows
Tool-using agents with bounded action spaces, dry-run modes and human-in-the-loop checkpoints. We design for failure modes first, capability second.
LLM evaluation & CI
Eval suites you can run on every PR, regression detection across model upgrades, A/B harnesses for prompt iteration. This is often the missing piece in an AI roadmap.
Analytics & data platforms
Modern stack (Snowflake, Databricks, BigQuery) with semantic layers, dbt models and dashboards your executives use.
Privacy & safe AI
PII detection, redaction, on-prem and air-gapped deployments, prompt injection defense. Compliance-aware from day one.
How an engagement runs.
Five phases, each with a measurable outcome, so you always know where the work stands.
How we approach AI projects.
We deliver working systems to production
Every engagement targets a deployed system with monitoring, evals and a runbook. A system that only works in a demo is not the goal.
Every feature starts with an evaluation
We won’t build features without a measurable acceptance test. If we can’t evaluate it, we’ll tell you why and propose what would make it measurable.
Senior engineers do the senior work
The architect named on the proposal is the architect who works on your project.
Frequently asked questions.
Have a question we haven’t covered? A 30-minute call with a tech lead can answer it.
Ask us directlyHow is this different from hiring an LLM consultant?
Most LLM consultants stop at a working prototype. We commit to deployment, on-call, and the infrastructure work that determines whether your AI feature is still working in six months.
Do you work with our existing models, or do we have to use yours?
We’re model-agnostic and provider-agnostic. We’ve built on OpenAI, Anthropic, Bedrock, Vertex, Azure OpenAI, and self-hosted Llama and Mistral. We pick what fits your latency, cost and compliance constraints.
Can you guarantee no hallucinations?
No, and you should be skeptical of anyone who does. What we can guarantee is a citation-and-evidence pattern, eval-driven regression catching, and human-in-the-loop checkpoints that keep error rates inside your tolerance.
Do you do model fine-tuning?
When it’s the right answer. Most of the time, better retrieval, better prompts and better evaluation outperform fine-tuning. We’ll tell you when fine-tuning is worth it.
What does the engagement cost?
Discovery sprints run $24–48k. Most full engagements run $180k–$450k for the first three months. We share a detailed estimate after the discovery sprint, not before.
Let’s talk about your project.
Tell us about your team, your stack and the problem you want solved. We’ll come back with how we would approach it.
Response time
Within one business day
Prefer a call?
Headquarters
Charlotte, North Carolina
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