The AI agencythat ships, retrieves, scores, drafts, monitorsAI features, not slide decks.
An AI agency that ships LLM features into your product, your CRM and your ops. Model selection, RAG on your real data, evals from day one, costs you can audit. We deploy AI where the work already happens — never as a separate dashboard nobody opens.
ActiveCampaign
Adalo
AdCreative.ai
Ahref
Airtable
Allo (The Mobile First Company)
Anthropic
Apify
Apollo.io
Attio
Base44
Baserow
Brevo
Bright Data
Browse AI
Bubble
CaptainData
ChatGPT
Claude
Claude Code
Claude Cowork
Clay
Clickup
Cursor
Deepseek
Dust
ElevenLabs
Fillout
Flutterflow
Folk CRM
Freepik Spaces
Gamma
Gemini
Glide
Grok
HiggsfieldAn AI feature that actually ships stands on 4 pillars.
Most AI pilots die between the demo and the rollout for the same reasons: wrong model picked for the task, no retrieval on real data, no eval suite, no cost monitoring. The stack we deploy in 2026 closes all four gaps from day one.
- Strategy
Use-case + model selection
We start at the business problem, not the model. We score candidate use cases on value, feasibility and unit economics, then pick the model that fits — Claude Sonnet for reasoning, GPT-4o for vision, Mistral for EU data residency, Llama 3.x on-premise for sensitive workloads. Model choice per task, never per fashion.
How we pick the right model - Integration
RAG, retrieval + fine-tuning
AI features die when the model can't see your data. We wire retrieval-augmented generation on your real corpus (Notion, Drive, Confluence, support tickets, CRM notes), build the embedding pipeline, set the chunking strategy, and only fine-tune when retrieval alone hits a ceiling. The model reads your stuff before it answers.
See the data pipeline - Deployment
Inside the product, not next to it
AI features live where the team and the user already work. A sidebar in the CRM, a slash command in Slack, an inline action in Webflow or Notion, a webhook reply on a Stripe event. No standalone "AI dashboard" nobody opens. The AI shortens the path the user was already taking.
See the integrations - Monitoring
Evals, cost + guardrails
Every AI feature ships with an eval suite (30 to 80 input/output pairs), output filters (refusal, length, cost ceiling), and a logging pipeline you can audit. If a model upgrade silently regresses quality or unit cost drifts above $0.20, you catch it the same week, not the quarter after.
What we measure
What an AI feature in prod actually moves.
- $0.04Avg cost per AI call
On a well-prompted Claude or GPT-4o feature with retrieval and 1-2 tool calls. We benchmark every deploy. If unit cost drifts above $0.20 the eval pipeline alerts us before it shows up on the invoice.
- −70%Time on the workflow
Across the 3-5 use cases we typically ship on a mission — content drafting, ticket triage, RFP scoring, sales research, knowledge retrieval. The team handles only the edge cases.
- 4-6 wkFirst feature in prod
From audit to a live AI feature inside your existing product. Week 1 audit, week 2-3 design + RAG setup, week 4-5 build + eval, week 6 deploy with kill switch. If an agency promises <2 weeks, they're skipping evals.
Our 4-step build, from use case to production.
We treat every AI feature as a small software product, not a prompt engineering experiment. Same shape regardless of whether the feature lives inside HubSpot, Zendesk, your app, or a custom internal tool.
- Discover · score candidate use cases on value, feasibility and unit economics
- Design · system prompt, RAG schema, eval set, cost ceiling, all written before code
- Build · feature wired in your existing app via SDK, MCP or no-code orchestration
- Deploy · embedded in your CRM, app, Slack or product surface — never standalone
We ship features into your product, not slides into your inbox.
Most AI consulting ends with a deck and a roadmap. We ship features that users hit on real workflows. Every mission is measured by the number of AI features running in production at month 3, not by the depth of the strategy doc.
- We ship LLM features into your product, not slide decks into your inbox
- Model picked per task, re-benchmarked every 6 months when a new generation lands
- RAG on your real corpus, evals on real inputs, monitoring on real costs
- Every prompt versioned, every call logged, every cost line attributable
We score your AI use cases, you leave with a plan.
Before quoting anything, we spend 60 minutes mapping the use cases where AI would actually move the needle, and ranking them on value, feasibility and unit economics. You walk away with a ranked candidate list and the design draft for the first feature — yours to ship in-house or with us. Zero pitch, just an outside look at where AI is actually worth deploying.
- Use-case scoring on every AI candidate you flag
- Top 3 candidates with cost-per-call estimate and expected ROI
- Design draft for the first feature (model, RAG schema, eval set)
- Honest take on the use cases where AI would be worse than status quo
How we run an AI engagement.
Five steps, in order, no skip. We don't open an editor before the design doc is signed, we don't deploy without an eval pass, and we don't bill a retainer before the first feature is running in production. Every step has a DOD and you approve before we move to the next.
- Step 1 · AI audit
Audit where AI actually moves the needle
We sit down with the people doing the work — product, ops, sales, support, content — and score every candidate process on three axes: business value (how much time or revenue is on the table), feasibility (does the model technology actually solve this in 2026), and unit economics (cost per call vs. status-quo cost). Most teams have 3 to 5 obvious AI wins they were too close to spot, plus a list of pet ideas where AI would be worse than the status quo. You walk away with a ranked candidate list and three quick wins to ship inside 30 days.
- Step 2 · Model + data design
Pick the model, design the data pipeline
Model picked per task, not per brand. Claude Sonnet 4.x for long-context reasoning, GPT-4o for multimodal and voice, Mistral Large for French and EU residency, Llama 3.x or DeepSeek on-premise when data legally can't leave your perimeter. Then we design the data flow: which corpus the model needs (Notion, Confluence, Drive, support tickets, CRM notes), how to chunk and embed it, how to refresh it, when to fall back to fine-tuning. RAG schema, embedding model, vector DB, refresh cadence — all signed off before a line of code.
- Step 3 · Build + eval
Build the feature with an eval suite from day one
Feature wired via the right runtime: SDK calls in your existing app for tight latency, MCP servers when the model needs to act on multiple systems, n8n or Make when ops will need to extend the workflow without code. Eval suite written alongside the prompt — 30 to 80 representative input/output pairs the feature has to clear before promotion. Cost benchmarked per call from the first build. If unit cost is wrong by 5x, we catch it before deploy, not on the next AWS invoice.
- Step 4 · Deploy in-product
Deploy the feature inside the product, not as a SaaS aside
AI features live where the team or the user already lives. A sidebar in the CRM, a slash command in Slack, an inline action in a Notion doc or Webflow CMS, a webhook reply on a Stripe event, a chat panel embedded in the product. No standalone AI dashboard nobody opens. We deploy with a kill switch, a feature flag and a graceful fallback so we can roll back in 30 seconds if the eval regresses.
- Step 5 · Eval, cost, monthly iteration
Run the eval, watch the cost, iterate every month
Eval suite from step 3 runs on every prompt change and on a daily cadence. Costs tracked per feature per day (Helicone, Langfuse, custom logging into Supabase or BigQuery). Refusal rate, hallucinated outputs, response length distribution, latency, fallback rate, weekly cost per active user — all on a shared dashboard. Monthly review with us: what to extend, what to retire, what model to migrate to. Features get sharper with the months, they don't decay.
The same stack, across multiple client features.
The frames below are pulled from real monthly review calls with clients running AI features in production: eval pass rate refresh, cost-per-call trends, model migration plans, the queue of new use cases to extend the feature set. Same operational rigor, different industries, all in B2B SaaS, services and ops. Our Trustpilot reviews come from the operators we work with.
- Monthly eval review with every client running 1+ AI features in prod
- Cost-per-call dashboard updated in real time, no quarterly slide deck
- An eval regression triggers a rollback before the next deploy
- Trustpilot reviews come from the operators using the features, not from marketing
The 10 questions we get asked on every call.
What's the difference between an AI agency and a generic IT consultancy?
A generic IT consultancy ships you a deck, a roadmap and a 6-month engagement that ends in 'recommendations'. An AI agency ships AI features into your product. Concrete output: a sidebar in your CRM that drafts replies, a slash command in Slack that summarizes a thread, a webhook that scores incoming RFPs, a chat panel embedded in your app. Measured by features in production and unit cost per call, not by hours billed. If the proposal mentions 'AI strategy' more than 'AI features shipped', it's consulting wearing AI cosplay.How much does an AI agency cost in 2026?
Depends on scope. A focused mission (one AI feature, one product surface, audit + design + build + deploy) runs $8,000 to $25,000 depending on integration complexity. A monthly retainer covering 3-8 features in production (extensions, evals, model migration, cost monitoring) starts around $4,000-$8,000/month. Watch out for agencies that quote in 'AI hours' or pitch a vague 6-month AI transformation — that's repackaged consulting. Our approach: free audit first, then a price per feature shipped, not per hour talked.Which model should we use — Claude, GPT-4o, Mistral or open-weights?
Depends on the task and the constraint. Claude Sonnet 4.x leads on long-context reasoning, clean tool use and refusing weird prompts cleanly. GPT-4o is faster on multimodal (vision, voice) and has the most mature function-calling tooling. Mistral Large is competitive on French language and EU data residency. Open-weights (Llama 3.x, DeepSeek, Qwen) work when you need data on-premises or your unit cost ceiling is sub-$0.01. We benchmark per use case and re-benchmark every 6 months when a new generation ships. The model is a choice, not a religion.RAG, fine-tuning or prompting — which one do we need?
Prompt engineering first — 70% of features ship with just a well-structured system prompt and good examples. RAG (retrieval-augmented generation) second — when the model needs to read your specific corpus before answering: docs, support tickets, CRM notes, internal wiki. Fine-tuning last — only when retrieval alone hits a quality or cost ceiling, typically for high-volume narrow tasks (classifier-style, fixed output schema). We start with the cheapest layer and only escalate if the eval says we need to. Most fine-tuning pitches we see are actually a RAG problem in disguise.How long does it take to ship a first AI feature in production?
Honest answer: 4 to 6 weeks for a first feature on a well-scoped use case. Week 1 audit + use-case scoring. Week 2-3 design (system prompt, RAG schema, eval set, cost ceiling). Week 4-5 build + integration into your product surface. Week 6 internal beta, eval pass, prod deploy with a kill switch. If an agency promises an AI feature in prod in 1 week, they're skipping evals — fine for a demo, dangerous in front of paying users.Will AI replace our team?
Augments. Every AI feature we ship has a fallback path back to a human operator — for the edge cases, the angry customers, the high-stakes decisions. What changes: the team stops doing the 80% of repetitive work the AI crushes and refocuses on the 20% that actually needs judgment. On the cohorts we've shipped: sales ops moves from CRM hygiene to building the playbook, support L1 moves from copy-paste replies to fixing the root cause that generated the ticket, content teams move from drafting to editing and ideation. Headcount stays, output multiplies.Is our data safe with LLM providers?
Depends on the provider and the contract. Anthropic and OpenAI both offer zero-data-retention modes on their enterprise APIs — your prompts and outputs are never used for training and aren't stored beyond the request. Azure OpenAI, AWS Bedrock and Google Vertex AI give you the same models running in your own cloud account, with EU or US data residency you control. For workloads where data legally can't leave your perimeter (finance, defense, healthcare), we deploy open-weights on-premise via vLLM or TGI. We pick the deployment pattern that fits your risk profile, not the cheapest one by default.What tools and CRMs do you wire AI features into?
Tool-agnostic. We've shipped AI features wired to HubSpot, Pipedrive, Salesforce, Attio, Folk, Airtable, Notion, Zendesk, Intercom, Slack, Gmail, Outlook, Stripe, Linear, GitHub, Webflow, Make, n8n, and custom internal systems via REST APIs or Postgres. The wiring lives behind an MCP server or a no-code workflow (Make / n8n) when the team will need to extend it without code. If you have a documented API and webhooks, we can wire AI to it.How do you measure ROI on an AI mission?
We track 6 main KPIs per shipped feature, reported monthly in a shared dashboard: usage (calls per day, daily active users), time saved per call (vs. status quo), unit cost per call, eval pass rate, refusal / fallback rate, and revenue or savings attributable to the feature. We refuse to track vanity metrics (model parameters, prompt token counts) unless they serve a direct business goal. If a feature isn't moving the needle after 8 weeks of iteration, we retire it instead of dragging it.How long do we commit for?
Three formats. (1) Audit only: flat fee, 2 weeks, deliverable is the ranked use-case list and the design doc for the first feature. (2) Build sprint: 4 to 8 weeks per feature shipped, fixed scope, fixed price. (3) Ongoing retainer: 6-month minimum for teams running 3+ AI features in production who want continuous eval, model migration and use-case extension. No forced annual contract, no convoluted exit clauses. If we don't ship, you stop.
Stop pitching the AI roadmap. Ship the first feature.
A 60-minute audit, three candidate use cases scored, one feature designed. If your team should build it in-house, we'll say so and hand over the design. If we're a better fit, we ship in 4 to 6 weeks.