Every question we get, answered.

Most consultancies sell decks and hours. We sell shipped systems — custom agents, content engines, voice AI — built on your data and handed back fully owned. Two-week build cycles, fixed-fee scopes, no permanent retainer required.

A scoped 2-week AI Sprint. Day 5 is a working demo on your data. Day 10 is the production ship plus runbook handoff. After that you choose: own it outright, run another sprint on the next workflow, or move to a light retainer for ongoing tuning.

Alongside. Most of our best work is co-built with internal engineers. We pair, lead, or just review — and we document the patterns so your team keeps shipping after we leave. No vendor lock-in by design.

First production system live in 2 weeks for a scoped sprint. Content and SEO engines start compounding traffic in 30 to 90 days. Strategy roadmaps are usually delivered inside the first week.

Tell us about the workflow you want to fix on the start a project page. We reply within one business day with a fixed-fee scope or an honest "this is not a fit" — never a sales sequence.

We start with a free 30-minute scoping call. Within 24 hours we send a written scope with a fixed price, timeline, and deliverables list. No hourly billing. If the scope changes mid-sprint, we talk — we do not just add to the invoice.

Fixed fee per sprint, paid in halves. Scopes start around fifteen thousand and most land between twenty-five and sixty thousand depending on integration depth. No retainers required.

Many clients start with a single sprint and then move to an ongoing retainer or stack additional sprints. You can also re-engage us for a new fixed-fee phase whenever you need more.

No. We take fixed project fees, paid in halves. No required retainers, no equity, no kickbacks from model providers or platform vendors. That keeps our recommendations honest.

A small, senior team of engineers, designers, and AI practitioners who have shipped production AI systems at scale. Every engagement is staffed by people who write code — not account managers handing work to a queue.

We work remotely with teams across North America and Europe. Most of our clients are companies between Series A and post-IPO that need AI shipped without rebuilding their engineering org around it.

Three ways: (1) we ship working systems in your stack rather than slide decks, (2) we work fixed-fee in two-week scopes so you always know cost and timeline, (3) we open-source the patterns we build by giving them away as free tools and templates on this site.

Internal copilots and custom agents on your data, voice AI and conversational systems, programmatic SEO and autoblogging engines, RAG pipelines, document intelligence, and AI-driven internal operations tooling.

A working system in your stack: deployed code, monitoring, prompts, evals, and a runbook your team can own. We hand off git access and walkthrough recordings — never just a slide deck.

Tell us anyway. Our published capabilities are pattern-buckets, not a fixed menu. If your problem fits the way we work — bounded, measurable, AI-shaped — we will scope it.

Most of our work is under NDA. We share anonymized receipts on request and can connect you with reference clients in your industry once a scope is in motion.

Yes. No credit card, no trial period, no hidden upgrade wall. Our consulting revenue funds the free tools and we intend to keep it that way.

Not at all. We handle the technical details. You just need to know the business problem you are trying to solve — we will figure out whether and how AI fits.

Read the blog for opinions and the resources hub for full-length guides. Every free tool also links to the related implementation pattern.

Almost always RAG. Fine-tuning teaches a model behavior and style — not new facts. If you need the model to answer using your documents, policies, or products, RAG is the right architecture. We fine-tune in narrow cases: a very specific output format, a high-volume classification task, or a latency-sensitive pipeline where a smaller specialized model is needed.

We are model-agnostic by design. Most client systems ship on Claude Sonnet or GPT-4o for primary reasoning, with cheaper tiers (Claude Haiku, GPT-4o-mini) for classification and routing. We always benchmark multiple models on your specific task before committing to an architecture — the right model is the one that actually works on your data, not the one with the best benchmark score.

With architecture, not hope. The three-layer approach: (1) ground the model in retrieved source documents so it answers from your content, not its training memory; (2) structured outputs with schema validation so answers commit to verifiable fields; (3) an LLM-as-judge eval that checks each claim against the source before it reaches users. "Don't hallucinate" in the system prompt does almost nothing.

We build an eval harness as part of every engagement — not as an afterthought. This includes a fixed test set of representative inputs with expected outputs, automated metrics (exact match, regex, LLM-as-judge), and a baseline measurement before any model work starts. That baseline is the only honest way to show whether your AI feature actually improved anything.

Yes. Most of our engagements involve connecting AI to systems that already exist: CRMs, ticketing systems, document stores, databases, internal APIs, and communication tools. We work in your stack, not a parallel one. The deliverable is code that runs in your infrastructure, not a standalone product.

Every production system we ship includes input and output guardrails: content classifiers for sensitive categories, PII detection and redaction before third-party model calls, schema validation for structured outputs, and permission scoping for any agentic tool use. Guardrails are not optional features — they are part of the production definition.

We treat prompts as production code: version-controlled, tested against a fixed eval set, and changed only with measurement. Good prompts are specific (clear role, explicit format, few-shot examples), not long. Bad prompts are long lists of "don't do X" that the model dutifully ignores under pressure. We build a prompt-testing workflow into every engagement so improvements can be validated, not guessed.

Not if you do not want it to. We exclusively use API tiers with zero-retention and no-training policies for client work — OpenAI's Enterprise/business tiers and Anthropic's commercial API both exclude customer data from model training. We document the data flow for every system we build and help you get sign-off from your legal and security teams.

Yes. For clients with strict data residency requirements we deploy open-source models (Llama 3, Mistral, etc.) on their own infrastructure or in their own cloud VPC. The quality gap versus frontier APIs has narrowed significantly — for many tasks a well-tuned 70B model in your environment outperforms a generic frontier model.

PII gets detected and redacted before it leaves your environment to reach any third-party model. We implement entity recognition to flag names, emails, SSNs, and custom PII types; substitute pseudonymous tokens in the model request; and restore them in the output if needed. Logs are stored in redacted form. For HIPAA-covered workflows we implement the full BAA chain.

We follow OWASP LLM Top 10 guidance as a baseline: prompt injection defenses on all inputs, secrets kept out of context windows, minimum-permission tool scoping for agents, output validation before downstream actions, and audit logging for all model calls. Every system ships with a threat model and a runbook for the security incidents most likely to happen.

It depends on the task — and the honest answer is "probably both." GPT-4o is strong for voice, vision, and broad tasks. Claude Sonnet is stronger for writing, long documents, and the most reliable function-calling. Gemini 2.5 Pro has a 2M-token context window that is genuinely useful for massive document analysis. Most production systems we build route between two or three models by task rather than betting on one.

Start with a chatbot. The step-change in complexity from a chatbot to a true agent — state management, tool error handling, loop detection, cost control — is significant. Most "agent" requirements turn out to be achievable with a well-designed chatbot plus one or two API calls. Build the agent when the chatbot genuinely cannot handle the task, not as the starting point.

RAG (Retrieval-Augmented Generation) is the pattern of looking up relevant documents from your own data and feeding them to the model before asking it to answer. Use it whenever you need the AI to know things from your business: product documentation, support history, internal policies, customer data. It fixes hallucination for domain-specific questions, keeps answers up to date, and enables citations.

Token cost is almost never the real cost — engineering time, evaluation time, and maintenance are. That said, the unit economics matter at scale. Two levers most teams miss: prompt caching (90% cost reduction on repeated context) and model routing (use cheap models for easy requests, expensive ones only for hard ones). Together these typically cut API bills 60-80% with no quality loss. Use our AI cost calculator to model it.

For almost all companies: no. Self-hosting requires GPU procurement or cloud GPU budgets, inference framework setup (vLLM, TensorRT-LLM), autoscaling, monitoring, and ongoing model updates. You need a real ML infrastructure team to operate it responsibly. The break-even point is typically 10M+ API calls per month and steady-state volume. The cases where it makes sense: strict data residency requirements, very high volume, or a compliance environment where third-party APIs are blocked.