Scopedbeforeyousign anything.
Every capability below states what it includes, exactly what lands in your hands, and how long it takes. No “it depends” until we've actually looked at your systems.
Find your problem. Then read the detail.
Custom LLM Integration
Most teams don't need a new app — they need language intelligence inside the systems they already run. We embed LLMs behind your existing interfaces and APIs, grounded in your private data, with the guardrails production demands.
- Working integration in your product or workflow, behind a feature flag
- Evaluation harness with task-level accuracy and cost benchmarks
- Prompt and guardrail library under version control
- Runbook covering monitoring, fallbacks, and model-swap procedure
The cheapest thing that works
wins.
Four system classes, honestly compared. Most engagements start with the simplest one that solves the problem and earn their way up — we'll tell you when you don't need the expensive option.
| Assistant / Copilot | Autonomous Agent | Predictive Model | RAG Knowledge Base | |
|---|---|---|---|---|
| What it is | Answers, drafts, and recommends inside a conversation or your product UI — a human executes every consequence. | Plans and acts across systems — reads queues, calls APIs, updates records — with approval gates where risk sits. | Learns from your historical data to forecast demand, score risk, or flag anomalies before they surface. | Retrieval over your private documents with cited, permission-aware answers — the model sees only what the asker may. |
| Best for | Knowledge work, drafting, support deflection, internal Q&A | High-volume, mostly-mechanical workflows across systems | Forecasting, scoring, anomaly detection, planning | Policy/document Q&A, research, onboarding, compliance lookup |
| First working version | 2–4 weeks | 3–8 weeks | 4–10 weeks | 2–3 weeks |
| What it needs from you | Your documents or knowledge sources; light integration | API access to your systems; a mapped workflow; volume that justifies it | Historical data in usable condition — we audit this first | Existing documents — messy is fine, missing is not |
The questions you were
going to ask anyway.
It ranges honestly. A RAG assistant over existing documents can be live in 2–3 weeks. An autonomous agent with deep system integrations typically takes 3–8 weeks to supervised production. Legacy modernization is phased — first extracted capability in 4–8 weeks, the full roadmap over months. We scope precisely during discovery and show working demos throughout, so you're never guessing at progress.
For individual productivity, you should — it's excellent. The gap appears at the system level: ChatGPT doesn't know your data, can't act inside your CRM or ERP, has no audit trail, and offers no guarantees about where your customers' data goes. We build AI that's grounded in your private data, wired into your workflows, and governed to your compliance requirements. Different tool, different job.
Less than you fear, more than zero. RAG systems work with the documents you already have — messy is fine, missing is not. Custom predictive models need historical data, and we assess whether yours is sufficient during the audit before you commit to model work. If your data isn't ready, we tell you that and fix the pipeline first — training on bad data just automates your existing errors.
Yes — that's half our practice. We connect through APIs where they exist and build facades where they don't, including mainframe and on-prem systems. The strangler-fig approach means your legacy core keeps running untouched while new capabilities wrap around it. Zero-downtime is an engineering requirement we design for, not a marketing phrase.
Architecture-first, not policy-after. Options include private model endpoints, on-premise or VPC deployment, PII redaction before any model call, and audit logs for every AI action. For regulated industries we map controls to HIPAA, SOC 2, or GDPR during design — and our security practice red-teams AI features for prompt injection and data leakage before launch.
With a discovery sprint — typically two weeks. We map your workflows and data, rank the opportunities by value and feasibility, and produce a concrete proposal with architecture, timeline, and cost. You own everything it produces. If the honest answer is that AI isn't your bottleneck, the discovery report says so.
AI systems degrade without care — models drift, prompts rot, costs creep. Every build ships with monitoring, an operations runbook, and a handover so your team can run it. Beyond that, we offer ongoing optimization retainers, but they're optional: we build for your independence, not our lock-in.
Claude, GPT, Gemini, and open-weight models like Llama and Mistral — selected per use case on accuracy, latency, cost, and data-privacy requirements, tested against your actual tasks. We're vendor-neutral by design, and every build includes a model-swap procedure so you're never locked to one provider's pricing or roadmap.
Not sure which one you need?
That's normal, and it's the first thing we work out together. Bring the problem, not the solution.