01
Retrieval-augmented generation
RAG systems over private documents, support knowledge bases, code, or customer data. Built around evaluation, not vibes — we measure retrieval quality and end-to-end answer quality before claiming the system works.
06AI & RAG
AI Automation & RAG Development
LLM features, retrieval-augmented generation, and workflow automation built on top of your private data — designed for the ones that earn their compute cost, not for the demo.
Who this is for
Operations leaders, product managers, and CTOs evaluating LLM integration — from internal copilots to customer-facing AI features — who want a partner that will tell them which problems are worth solving with AI and which are not.
What we solve
Most AI projects fail not because models are insufficient, but because the team did not budget for evaluation harnesses, retrieval quality, prompt versioning, cost monitoring, or fallback behavior. We treat those as first-class engineering work, not afterthoughts.
We integrate AI where it makes economic sense — and we tell clients honestly when it does not. The most useful AI features in production are unglamorous: classifying documents, extracting structured data from messy inputs, surfacing the right knowledge to the right person at the right time, and automating workflows that used to need a human in the loop. We build those, with the operational hygiene that production AI actually requires.
What we build
The systems we've shipped most often.
02
Document AI
Extracting structured data from invoices, contracts, claims, IDs, and other messy inputs. LLM-driven where flexibility matters, classical OCR + rules where it is cheaper.
03
Internal copilots
AI assistants for support, sales, ops, or engineering teams. Hooked into the systems they actually use, with proper auth, audit logging, and a way to course-correct when the model gets it wrong.
04
Customer-facing AI features
Chatbots, search assistants, classification, recommendations — designed with the rigor a customer-facing feature requires (latency budgets, fallback paths, cost guardrails, abuse mitigation).
05
Workflow automation
End-to-end automation of human-in-the-loop processes — review queues, approvals, escalations, and the operational tooling that makes AI safe in real workflows.
06
Evaluation harnesses
Continuous evaluation of model output quality, retrieval precision, and cost — so you actually know whether the next model upgrade or prompt change is an improvement.
Capabilities
How the team is set up.
LLM integration
Multiple models, abstracted behind a clean interface so swapping providers (OpenAI ↔ Anthropic ↔ open-weights) is a config change, not a rewrite. Prompt versioning, response caching, and cost telemetry built in.
OpenAIAnthropicMistralLlamaTogetherReplicate
Retrieval & vectors
Hybrid retrieval (semantic + keyword + metadata filters), chunking strategies tuned to the document type, and the evaluation discipline to know when retrieval is good enough.
pgvectorPineconeWeaviateQdrantElasticsearchCohere Rerank
Orchestration
Agentic flows kept under tight control — explicit state machines over implicit agent loops where reliability matters. Tool use, function calling, and structured outputs as first-class concerns.
LangChainLlamaIndexDSPyInngestTemporalOpenAI Functions
70%
Proof
reduction in manual document processing time
Logistics document automation — receipts, customs forms, and waybills routed through extraction + review.
Selected work
All case studiesWhere we've done this work.
03Logistics
Document AI for a freight forwarder — 800/day, multilingual
A 12-person ops team manually classifying 800+ shipping documents a day became a 4-engineer + LLM pipeline doing it in minutes, with a 0.3% error rate. Customs holds dropped 80%.
70%less manual processing time
05E-Commerce
Consolidating $5M+ in paid media spend, in-house, with real attribution
DTC brand spending $5M+ across four agencies, ROAS deteriorating, attribution opaque. We brought paid media in-house, built first-party analytics, and automated inventory-aware ad serving.
4.2×average ROAS sustained over 18 months
Process
Full delivery processHow we run this work.
01
Discovery
We ask the questions no one else asks. Business model, technical constraints, team capabilities, real deadlines. We read the documentation you haven't written yet.
02
Strategy
Architecture decisions made before a single line of code. Stack selection, deployment model, third-party dependencies — documented, debated, decided.
03
Build
Iterative, with weekly demos. No black-box sprints. You see working software every week or we're not doing it right.
05
Scale
Growth creates new problems. We stay engaged — performance tuning, infrastructure scaling, feature iteration. The relationship doesn't end at launch.
FAQ
Common questions
Which LLM should we use?+
Most production workloads benefit from a mix. We typically default to OpenAI or Anthropic frontier models for quality-critical paths, with smaller open-weights models (Llama, Mistral) for high-volume or privacy-sensitive workloads. The right answer depends on quality requirements, latency budget, cost ceiling, and where the data can live. We benchmark options during Strategy.
RAG vs. fine-tuning?+
RAG (retrieval-augmented generation) is almost always the right starting point — cheaper, faster to iterate, and updates as your data does. Fine-tuning is useful for narrow, repetitive tasks where you have many labeled examples and need lower latency or smaller models. Most successful production AI is RAG with light fine-tuning around the edges.
How do you handle data privacy?+
We work in regulated environments (FinTech, healthcare) routinely. Options range from API-based providers with zero data retention agreements, to private deployments via Azure OpenAI, AWS Bedrock, or GCP Vertex, to fully self-hosted open-weights models on your own infrastructure. We pick the deployment model based on the regulatory and contractual constraints, not on the latest blog post.
How do you deal with hallucinations and incorrect outputs?+
Three layers. (1) System design — RAG with high-quality retrieval, structured outputs, and guardrails reduce the surface area for hallucination. (2) Evaluation — continuous offline and online evaluation catches regressions before users do. (3) Fallback — every customer-facing AI feature has a graceful degradation path when confidence is low.
What does an AI project actually cost in production?+
Cost is dominated by model inference, with retrieval and infrastructure as smaller contributors. We model expected cost per request during Strategy and instrument every call in production so cost is visible in real time. Most projects pay back within months because the alternative was paying humans to do the same task.
Related services
Often paired with this one.
01
Full-Stack Web Development
End-to-end web applications — from API design to deployment pipelines. React, Next.js, Node.js, and the rest of the stack you'll actually run in production.
Learn more05
Cloud Infrastructure & DevOps
Infrastructure that doesn't keep you up at night. AWS, GCP, Azure. Kubernetes when it earns its place. CI/CD with rollback. Cost-aware from day one.
Learn more02
Custom Software Development
Bespoke business systems built around the workflow you actually have, not the one a generic SaaS forces on you.
Learn moreReady to scope it?
Most engagements start with a 30-minute discovery call. No pitch deck, no NDAs on day one — just an honest conversation about your problem.
Schedule a Call