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AI and Automation

AI That Does the Work, Not Just the Demo

We build chatbots, agents and automations that run on your data and your tools, and we measure whether they actually save time.

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What Can AI Automation Do for Your Business?

AI automation takes over repetitive work that follows a pattern: answering common questions, reading documents, moving data between tools and flagging what needs attention. Your team keeps the decisions, and the routine steps run on their own, around the clock, with a record of everything done.

  • Chatbots and Assistants

    Support and sales assistants that answer from your own documents, hand off to a person when unsure, and log every conversation for review.

    Website ChatWhatsAppHelp Desk

  • Workflow Automation

    We connect the tools you already pay for so data moves on its own: new leads enriched, invoices filed, reports sent without copy and paste.

    n8nMakeZapierCustom APIs

  • LLM Features in Your Product

    Summaries, smart search, drafting and classification added to the software your customers already use, with cost and speed budgets agreed upfront.

    OpenAIAnthropicGoogle

  • Document Processing

    Invoices, contracts, forms and IDs turned into structured data. Low-confidence fields go to a review queue instead of straight into your system.

    OCRExtractionReview Queue

  • AI Agents

    Agents that use tools, such as your CRM, database or ticketing system, to finish multi-step tasks, with limits on what they can change without approval.

    Tool CallingMemoryApprovals

  • Prediction and Analytics

    Churn risk, demand forecasts, recommendations and anomaly alerts built on your historical data, delivered as a dashboard or an API.

    ForecastingChurnAnomalies

Where Teams Use It First

Good first projects are frequent, rule-based and easy to check.

  • Customer Support That Answers at 3 am
  • Invoice Processing
  • Lead Qualification
  • Weekly Reports Written for You
  • Contract Risk Flagging
  • Inventory Alerts
  • CV Screening
  • Maintenance Warnings

How Does a Reliable AI Assistant Find the Right Answer?

It uses retrieval-augmented generation, or RAG. Instead of relying on what the model memorized, the system first finds the relevant passages in your own documents, then writes an answer from those passages and cites them. Most of the quality comes from how well that search step is built.

  1. Ingest and Clean

    Pull documents from Drive, Notion, a help center or a database, then strip headers, footers and duplicates that would confuse the search.

  2. Chunk and Embed

    Split content along its real structure, like headings and clauses, and keep details such as source, date and owner with every piece.

  3. Index with Permissions

    Store everything in pgvector, Qdrant or Pinecone, filtered so each user only finds what they are allowed to see.

  4. Retrieve and Rerank

    Combine keyword and meaning-based search, then rerank the results so the model reads the five best passages, not the fifty closest.

  5. Answer with Citations

    Every answer links to the passages it used, so your team can check it in one click and trust grows for the right reasons.

  6. Guard and Evaluate

    Redact personal data, check answers against their sources, and run a nightly test set so a prompt change can’t quietly make things worse.

What We’ve Learned Shipping AI to Production

A demo works on ten friendly questions. Production has to survive thousands of real ones.

  1. Test Sets Beat Gut Feeling

    Before anything ships we build a set of real questions with known good answers. Every change is scored against it, so quality is measured, not guessed.

  2. Search Decides Answer Quality

    Most bad answers come from the wrong context, not the wrong model. We spend our effort on chunking, metadata and reranking first.

  3. Pick the Model per Task

    A small, cheap model can sort and route requests. The expensive one only handles the hard steps, which keeps running costs down.

  4. Plan for the Wrong Answer

    Confidence thresholds, fallbacks and a clear handoff to a person are designed on day one, not added after the first complaint.

How Long Does an AI Project Take?

The first version of an AI assistant or chatbot typically goes live in 4 to 6 weeks, as in the steps shown here. Larger systems, such as agents that act inside several tools, typically take 6 to 10 weeks. You see a first demo within the first two weeks, and get monitoring after launch so quality doesn’t drift once real users arrive.

  1. Discovery Week 1

    We map the manual work, estimate the hours each candidate would save, and pick the task with the clearest return to start with.

  2. Architecture Week 2

    Model choice, data flow, integrations and security are written down, and you approve the plan before the main build starts. You see a first demo on your own data within the first two weeks.

  3. Build Weeks 3 to 4

    Short sprints, with a written update every week and a demo of working software every two weeks, so you see real behavior on your own data early.

  4. Test Week 5

    We run the test set, try edge cases and deliberately misleading prompts, and confirm every fallback works.

  5. Launch Week 6

    Deployment, documentation and a training session for the people who will use and maintain it.

  6. Monitor Ongoing

    Dashboards for cost, speed and answer quality, with a monthly review of what to improve next.

Models and Tools We Build With

We choose per project, based on accuracy, cost and where your data is allowed to go.

How Much Does AI Automation Cost?

Fixed-Price Projects typically start from $3,000 for a single focused automation or chatbot. Systems with several integrations or agents cost more, and every project begins with a written fixed-price proposal. Ongoing monitoring and improvement runs on a monthly basis, as a Dedicated Team or Individual Developers, who typically start from $1,500 per month per developer.

Running costs, meaning model and hosting fees, are estimated before the build and tracked on a dashboard afterwards. If you would rather have engineers work inside your own team, you can also hire AI developers.

Frequently Asked Questions

  • Can You Add AI to Software We Already Have?

    Yes. We connect language models to your existing CRM, help desk, database or web app through APIs, so your team keeps the tools they know and gains new features inside them.

  • Is Our Data Used to Train AI Models?

    Not by us. We use providers’ API tiers whose terms exclude training on your data, and we confirm the current terms with you. We can also host open models on your own servers when data must not leave them. Access controls, logging and personal data redaction are part of the design.

  • What Happens When the AI Gives a Wrong Answer?

    We design for it. Answers below a confidence threshold go to a person, sources are shown so mistakes are easy to spot, and every reported error is added to the test set so the same mistake is caught before the next release.

Related Services

Find the First Task Worth Automating

Tell us what your team does by hand every week. We’ll tell you what AI can take over, what it shouldn’t, and roughly what it would cost.

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