AI assistants, automation workflows, and analytics layers wired into the systems your team already runs. Banking-grade discipline applied to LLMs, RAG, and production-ready automation — not just demos.
Every component shipped, integrated, monitored, and supported by one team.
AI assistants tuned for your workflows — customer support, internal Q&A, sales enablement, operational helpers — wired into your existing channels.
Retrieval-augmented generation grounded in your documentation, SOPs, and approved knowledge — accurate answers, not hallucinations.
Classification, extraction, and routing for high-volume document workflows — invoices, contracts, claims, applications.
Multi-step automation for operational processes — approvals, exception handling, escalation — across the tools your team already uses.
Custom dashboards, decision-support layers, and AI-assisted analytics on top of your operational data.
Model drift, latency, response quality, and compliance monitoring baked in from day one — not bolted on later.
We map the workflow, identify the highest-leverage AI intervention, and validate it can deliver real business value before any build starts.
We assess your data infrastructure — quality, accessibility, governance — and define what’s needed for the AI to work in production.
We build on pre-trained models (OpenAI, Claude) or custom-tuned options as appropriate, wired into your stack, your data, your tools.
Production deployment into your cloud, your security boundary, your compliance envelope. Not a hosted demo.
Output quality, model drift, cost, and compliance tracked from go-live. Guardrails are in place; incident response is part of the engagement.
Knowledge bases stay current, models get tuned, new capabilities ship through scheduled releases. AI doesn’t stay frozen — neither do we.
Customer service AI, document triage, fraud signals, internal Q&A over compliance docs.
Student services AI, parent-portal Q&A, content recommendation, academic analytics.
Conversational commerce, customer support, product Q&A, demand-prediction analytics.
Internal helpers, workflow automation, document intelligence, decision-support layers across operational data.
For RAG and most pre-trained-model workflows, no — you can start with what you have. We’ll assess what’s needed for the specific use case during discovery. For custom-trained models, data quality matters more — but we’ll be honest about that trade-off upfront.
Primarily OpenAI and Anthropic (Claude) for production work. We integrate with Google (Gemini), Mistral, and open-source models where appropriate. We’ll recommend based on your use case, security posture, and cost profile — not because we’re locked into one vendor.
For our productized AI Operations Sprint, 60 days from discovery to production. For larger custom AI builds, typically 3-6 months depending on scope, integration depth, and compliance requirements.
Three layers: (1) RAG grounding so the AI references your approved knowledge instead of fabricating; (2) output evaluation and quality scoring to catch drift in production; (3) human-in-the-loop escalation for high-stakes outputs. AI doesn’t make decisions it shouldn’t be making.
By default, we operate it. Monitoring, knowledge-base refresh, model updates, and incident response are part of the delivery model. If you prefer to take operations in-house after stabilization, we hand off with full documentation and runbooks.
The AI Operations Sprint is fixed-price. Custom AI builds are scoped and quoted as fixed-deliverable milestones. Operations engagements after go-live are typically monthly retainers based on scope. No open-ended T&M.
Whether it’s a new product, a stuck integration, or a system that needs a fresh team — we’d like to hear about it.
Read by the team within 24 hours. No drip sequences, no bots.
Or email info@braincrop.io