AI Solutions
Agents that handle real workflows. RAG over your knowledge base. Document AI for contracts and invoices. Voice AI for customer ops. Fine-tuned models when off-the-shelf isn't enough. Plus the evals, guardrails, and observability that keep it production-grade.
Six AI capability surfaces. We pick what your business actually needs — and say no to the rest.
Agents that take action — look up data, send emails, file tickets, run workflows. Tool use, memory, and recovery built in. Guardrails before, during, and after every action.
Examples: customer-support escalation, internal ops automation, sales-research agents, scheduling assistants.
Retrieval-augmented generation grounded in your real data. Vector + keyword hybrid search, citation tracking, freshness controls, and evals so answers stay accurate as your knowledge grows.
Examples: internal Q&A bots, support copilots, regulatory lookups, sales-enablement search.
Structured extraction from contracts, invoices, forms, statements, anything. We pair vision-capable models with validation rules so the output is something you can actually pipe into your systems.
Examples: invoice ingestion, contract review, form processing, claims intake.
Real-time speech-to-text, text-to-speech, and conversational voice agents. Used in production by clients in education and accessibility — including SpedUp's voice-powered reading engine.
Examples: phone-based customer ops, accessibility readers, IVR replacement, voice-driven internal tools.
Image and video understanding for image-heavy workflows. Detection, classification, OCR, and visual QA. Built with proper data pipelines and human-in-the-loop where stakes are high.
Examples: inventory recognition, photo-based onboarding, quality inspection, ID verification.
When off-the-shelf models aren't accurate enough, we fine-tune on your data — and back it with rigorous evals so you know exactly when the model is fit for production.
Examples: domain-specific classifiers, voice-of-customer summaries, regulated-language generation, internal style adoption.
Same engineering discipline we used at JPMorgan, NYT, and Ford — applied to AI builds.
Free 30-minute call. We understand your business, the problem you're solving, and whether AI is even the right answer. If it's not, we'll tell you. If it is, you walk away with a rough scope and budget.
We design the system: which models, which retrieval strategy, what guardrails, where humans stay in the loop. You approve the approach before we write a line of code.
We build in two-week sprints. Every sprint ships working software plus an updated eval suite — so you can see the model improving against your real data, not just demo data.
We deploy to production with cost monitoring, rate limits, error budgets, and observability. Ongoing support keeps the model accurate as your business and data evolve.
Real AI in production — not demos that fall over the moment they meet a real user.
Full-Stack Build · EdTech
Your child's reading mission starts here
What it looks like when a small business ships voice AI that actually works in production — with kids on the other end.
Platform (SaaS) · Retail / POS
Your entire store in your pocket
What custom software looks like for the actual store on the actual corner — not the version built for big chains.
Most engagements start at $25K (Sprint tier — focused build, 4–8 weeks). Larger systems with retrieval, fine-tuning, or agent orchestration land in the $50–100K range. We scope before we quote.
Your data lives where you want it to live — your cloud, your VPC, or our managed infrastructure. We default to provider data-handling agreements that don't train on your data (OpenAI, Anthropic, Gemini all support this). For sensitive cases we run open-weight models on your own infrastructure.
We design for it from day one. RAG grounds answers in your real data. Structured outputs constrain the model. Validation layers catch anomalies. Human-in-the-loop catches the rest. We ship evals that measure hallucination rate so you know it's actually working.
No. We're model-agnostic. Anthropic Claude for reasoning and tool use. GPT for general text. Gemini for multimodal. Open-weight models when fine-tuning makes sense. We pick the model for the task — and we benchmark.
Yes. We've deployed AI systems on customer-owned infrastructure. Open-weight models (Llama, Mistral, Qwen) run wherever you can run a GPU. We design the architecture to be portable across providers.
Monthly retainers ($5–15K/mo) cover model updates, eval drift monitoring, prompt iteration, and small feature work. AI systems need ongoing attention because models, data, and use cases all change.
Free Assessment
20 questions, 10 minutes. Score your AI readiness across data, team, process, budget, and strategic fit. We'll tell you where to start — and where AI probably isn't the right answer.
30 minutes. No pitch. We'll tell you if it's the right fit — and what we'd build if it is.
Tell us what you'd build