AI & Automation
Turn AI from a buzzword into real, measurable business value.
We help businesses ship AI features that actually work: LLM-powered products, RAG over your own documents, ML models trained on your data, and workflow automation that eliminates the grunt work. Real systems, not demos.
Quick Overview
Timeline
6-20 weeks
Starting At
$30,000
Capabilities
10 core capabilities
Engagement
Free consultation
Overview
What We Do & Why It Matters
Most businesses have run at least one AI pilot by now, and most of those pilots have stalled. The gap between a working ChatGPT demo and a reliable production feature is larger than it looks, and it is where most AI projects die. We specialize in closing that gap, taking an AI concept from a Jupyter notebook or a Figma mockup to a production system that handles real traffic, real edge cases, and real money on the line.
Our AI practice spans three categories. First, LLM-powered product features: chatbots, document Q&A, content generation, summarization, classification, structured data extraction, and agent-style workflows, built on OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, or open-source models deployed on your own infrastructure. Second, traditional machine learning: recommendation systems, fraud detection, churn prediction, demand forecasting, and computer vision, trained on your proprietary data with a proper MLOps pipeline. Third, workflow automation: the boring-but-lucrative category where we replace manual data entry, document processing, email triage, and reporting with reliable, observable, auditable software.
We approach every AI engagement with measurement first. In the kickoff week we agree on a quantitative success metric, accuracy on a held-out test set, tokens per task, dollars per resolved ticket, minutes saved per week, whatever actually maps to business outcome. We then build the system in an iterative loop of prompt engineering, evaluation, and refinement, with an evaluation harness running on every change. You get numbers, not vibes.
LLM features require a different engineering discipline than most software. We treat prompts as versioned code, store every input and output for offline analysis, run regression suites on every model or prompt change, use structured outputs with JSON mode or constrained generation, put guardrails around unsafe inputs and outputs, and build graceful fallbacks for when a model is slow, down, or produces malformed output. We use frameworks like the Vercel AI SDK, LangChain, and LlamaIndex where they help and write custom orchestration where they get in the way.
Data privacy is a first-class concern, not an afterthought. We deploy on your cloud accounts, use regional API endpoints to meet data residency requirements, configure zero-retention agreements with providers like OpenAI and Anthropic, and when the data is too sensitive for any third party we run open-source models like Llama, Mistral, or Qwen on your own GPU infrastructure. For regulated industries we ship with full audit logs, BAAs, DPAs, and SOC 2 Type II-aligned controls.
Cost control is a daily practice. A careless LLM integration can spend $50,000 a month on tokens for what a well-engineered system does for $5,000. We design for cost from day one: prompt compression, response caching with semantic hashing, routing cheap requests to smaller models and hard requests to frontier models, batch APIs where latency allows, and per-user or per-tenant quotas. Every deployment ships with a live cost dashboard.
We stay practical. We say no to AI for problems where AI is the wrong tool, and we say no to generative AI when a regex, a lookup table, or a traditional ML model would be cheaper and more reliable. The best engagement outcome is often a smaller, more focused AI deployment than the client initially wanted, because scope discipline is where ROI actually comes from.
Capabilities
What We Deliver
LLM Integration & Product Features
OpenAI GPT-4 and GPT-5 class, Anthropic Claude 3.5 Sonnet and 4 class, Google Gemini, and Llama or Mistral open-source models integrated into your product through the Vercel AI SDK, LangChain, or a custom orchestration layer, with streaming, structured outputs, tool use, and guardrails.
Retrieval-Augmented Generation (RAG)
Production RAG systems over your documents, wikis, databases, and Slack history using Pinecone, Weaviate, pgvector, or LanceDB for the vector store, OpenAI or Voyage AI embeddings, hybrid search with BM25 rerankers, and source citations the user can click.
AI Agents & Multi-Step Workflows
Agent-style features that call tools, query APIs, browse the web, execute code, and chain reasoning across multiple steps, built with the OpenAI Assistants API, Anthropic Tool Use, or a custom ReAct-style orchestrator, with checkpointing and human-in-the-loop approval gates.
Custom Machine Learning Models
Classification, regression, time-series forecasting, recommendation systems, and clustering trained on your data using PyTorch, scikit-learn, XGBoost, or LightGBM, with an MLOps pipeline covering experiment tracking, model registry, and automated retraining.
Conversational AI & Chatbots
Intelligent chatbots and virtual assistants with memory across sessions, integration to your knowledge base and CRM, multi-turn conversation handling, sentiment-aware escalation to human support, and voice support through ElevenLabs or Deepgram.
Computer Vision Systems
Image classification, object detection, OCR, visual inspection, and video analytics using OpenAI vision models, Anthropic's vision, GPT-4o, Gemini Vision, or self-hosted YOLO and Segment Anything variants for manufacturing, retail, insurance, and healthcare use cases.
Structured Data Extraction
Turning unstructured PDFs, emails, contracts, invoices, receipts, and forms into clean structured data using LLMs with JSON mode, OCR preprocessing, and a validation layer that catches hallucinations before data reaches your system of record.
AI-Powered Search & Ranking
Semantic search that understands intent, not just keywords, using vector embeddings plus lexical search plus a learned reranker, with personalization signals, filters, and facets that feel like Algolia but understand natural language queries.
Workflow Automation & RPA
End-to-end automation of repetitive processes using Zapier, n8n, Make, Temporal, or custom code: invoice processing, lead routing, report generation, form intake, and the long tail of internal busywork that drains operations teams.
Data Pipelines & MLOps
ETL and ELT pipelines through Airflow, Dagster, or Prefect, feature stores through Feast or Tecton, experiment tracking with Weights and Biases or MLflow, and deployment through SageMaker, Vertex AI, Modal, or Replicate.
Real Results
How We've Helped Businesses Like Yours
A legal firm needed to search across 50,000 contracts by meaning, not keyword, to answer questions like 'which of our master service agreements have a most-favored-nation clause?' We built a RAG system on pgvector with OpenAI embeddings, a legal-specific reranker, and cited quotes in every answer, cutting a research task that used to take a paralegal four hours down to under two minutes.
An e-commerce company wanted personalized product recommendations on their homepage and product detail pages. We trained a two-tower recommendation model on two years of browsing and purchase data, deployed it on SageMaker with sub-50ms inference, and A/B tested it against their existing Shopify recommendation app, lifting add-to-cart rate by 18 percent and average order value by 11 percent.
A customer support team at a B2B SaaS company was getting buried under 400 tickets a day. We built an AI triage system using Claude Sonnet that categorized every ticket, pulled related help-center articles, drafted a suggested reply, and auto-resolved the 30 percent that were simple password or billing questions, cutting average first-response time from four hours to nine minutes.
A property management company spent hours a week turning scanned invoices into QuickBooks entries. We built a document extraction pipeline using GPT-4o vision plus a validation layer, processing 2,000 invoices a month for about $40 in API spend and saving them 25 hours of manual work per week.
A healthcare platform wanted a HIPAA-compliant chatbot that could answer patient questions from their provider handbook. We self-hosted Llama 3 70B on their own AWS infrastructure with a BAA in place, built a RAG layer over their internal docs, added guardrails for medical advice, and integrated it into their patient portal with a clear escalation path to human nurses.
A fintech company needed to detect suspicious transaction patterns in real time. We built a fraud detection model combining XGBoost on structured features with an LLM-based review of unusual cases, deployed it behind a FastAPI gateway, and reduced their false positive rate from 8 percent to under 2 percent while catching more actual fraud.
A media company wanted to generate SEO-optimized article briefs and first drafts from a list of target keywords. We built an agent-style pipeline that researched the topic across cited sources, generated an outline, drafted the article with internal linking suggestions, and passed it to a human editor, tripling their content team's output.
A real estate platform needed an AI assistant that could answer buyer questions about listings using all their internal market data. We built a RAG system with hybrid search over listings, neighborhood stats, and historical comps, deployed as a chat widget, and saw a 28 percent lift in qualified lead conversion.
A SaaS company wanted their sales team to get automatic meeting prep briefs before every call. We built an agent that pulled CRM history, news mentions, LinkedIn activity, product usage data, and recent support tickets into a one-page brief delivered to Slack 30 minutes before each meeting, all for about $0.08 per brief.
A logistics company needed demand forecasting at the SKU-location level for 15,000 SKUs across 40 warehouses. We built a hierarchical forecasting model using LightGBM with external features like weather and promotions, cutting inventory carrying costs by 14 percent while reducing stockouts.
An insurance company needed to extract structured claim data from adjuster field reports, which were often handwritten, photographed, and emailed in. We built a vision-to-structure pipeline using GPT-4o plus a rules-based validation layer, processing 1,200 claims a week with 97 percent structured-field accuracy.
A marketing agency wanted their own proprietary AI tooling to generate on-brand copy, social content, and creative briefs faster than their competitors. We built an internal platform with fine-tuned brand voices per client, prompt templates, version control, and a browser extension that surfaced the tool inside Google Docs and Figma.
Technology
Our Tech Stack
Our Process
How We Work
Use-Case Discovery & ROI Model
A one to two week discovery phase where we interview stakeholders, identify the candidate use cases, evaluate data availability and quality, and build a written ROI model with conservative and optimistic estimates. Many engagements stop here with a recommendation to delay, pick a different use case, or invest in data infrastructure first, and that is a success outcome.
Data Audit & Evaluation Harness
We catalog your data sources, assess quality and access, and build an evaluation dataset and harness before we build the system. This is often the step most AI projects skip, and it is the main reason most AI projects fail. You cannot ship what you cannot measure.
Proof of Concept
A focused two to four week PoC to validate feasibility on your real data, measured against the evaluation harness. We share weekly notebooks, metrics dashboards, and honest assessments of whether the approach is ready to productize. If the PoC underperforms, we recommend against a full build.
Production System Build
Once the PoC clears its targets, we build the production system: API layer with FastAPI or tRPC, observability with Langfuse or Helicone, structured logging of every prompt and response, a prompt registry, evaluation pipelines in CI, cost monitoring, and rate limiting.
Integration & Human-In-The-Loop Design
We integrate the AI system into your product or workflow, design the human-in-the-loop review experience, and set up feedback capture so every user correction becomes training data. Agents always have an appropriate level of confirmation before they take a consequential action.
Monitoring, Evaluation & Drift Detection
Post-launch we track model performance against ground-truth labels where available and proxy metrics where not, watch for input drift, monitor cost per task, track latency percentiles, and run scheduled regressions against new model versions as providers release updates.
Scale, Optimize & Iterate
Once the system is stable, we optimize for cost and latency through prompt compression, semantic caching, smaller-model routing, and batch processing where applicable. Typical post-launch work reduces cost per task by 40 to 70 percent over the first two months.
FAQ
Common Questions
Ready to Get Started?
Let's discuss your ai & automation project. We'll review your requirements, answer your questions, and provide a clear proposal — no obligation, no pressure.
Projects starting at $30,000 · 6-20 weeks typical timeline