/ For founders scaling past chaos
You're just not sure where to start — or who to trust with it.
In 30 minutes, we'll map exactly which parts of your business AI can automate right now. Specific tools. Real timelines. No slide decks.
/ The cost of doing nothing
Your competitors who've moved have a compounding advantage
They're closing deals faster, operating with leaner teams, reinvesting the savings. Every month you wait is a month of that gap widening — and it compounds. A 3-month head start today becomes a 12-month lead by next year.
Every manual process compounds in cost as you scale
What takes your team 4 hours today takes 8 hours when you double in size. Hiring to handle manual work doesn't scale — it just gets more expensive. The ceiling is closer than it looks.
The longer you wait, the harder implementation becomes
AI systems trained on your data outperform generic ones. The earlier you start, the more data the system accumulates, the better it performs — and the harder it becomes for late movers to close that gap.
You're already paying — just not getting the output
Every hour a talented team member spends on a task a system could handle is a salary line for work that shouldn't require a human. It's not a future cost. It's a present one, billed every pay cycle.
If your team spends 2 hours per day on automatable work, that's 500 hours per person, per year — billed at salary, producing output a system could generate overnight.
At any salary.
The math is uncomfortable.
/ What you get in 30 minutes
This isn't a discovery call where we try to understand your business so we can pitch to you. You walk away with something concrete — whether you work with us or not.
Book your 30 minutes →Exact processes to automate first
Not 'explore AI possibilities' — specific workflows in your company that can be automated right now, ranked by impact and ease.
The right tools for your stack
Not just 'use ChatGPT.' We'll name the exact models, platforms, and integrations that fit your existing systems.
Real time and cost estimates
A specific range — not 'it depends.' We build fast and price fairly. You'll know exactly what to expect before any commitment.
An honest fit assessment
If we're not the right team for your project, we'll tell you directly. We'd rather lose a call than take a project we can't deliver well.
/ What we build
Every AI problem falls into a category. Here is every category we build in — the specific capabilities, the technologies we use, and the outcomes we've shipped. Click any domain to expand it.
Agentic workflow pipelines
Multi-step autonomous workflows that read inputs, make decisions, call APIs, and write outputs — without a human in the loop.
Email & CRM automation
Triage inboxes, draft replies, qualify leads, update CRM records, and route tasks — fully autonomous or human-in-the-loop.
Content & marketing automation
Blog generation, SEO optimisation, social scheduling, image creation — from a single prompt to a published post across channels.
Data extraction pipelines
Pull structured data from unstructured sources — websites, PDFs, spreadsheets, APIs — and pipe it into databases or downstream systems.
Scheduling & coordination bots
Calendar management, meeting coordination, reminder systems, and cross-system sync — no more manual scheduling overhead.
Invoice & financial automation
Extract data from receipts, generate professional invoices, apply tax logic, and file to accounting systems — end to end.
Custom object detection
Train YOLO or custom architectures on your specific objects — products, defects, people, vehicles — at production speed.
Manufacturing QC systems
Real-time defect detection and product classification on live conveyor lines. Integrated with PLC systems and industrial cameras.
Sports & motion analytics
Player tracking, speed/distance calculation, possession analysis, and heatmap generation from video footage.
Image segmentation
Pixel-level segmentation for medical imaging, satellite analysis, retail shelf analysis, and part identification.
Video processing pipelines
Frame-by-frame analysis, scene classification, motion detection, and event-triggered recording systems.
MLOps for vision models
Automated data collection, labelling pipelines, model versioning, continuous retraining, and performance monitoring in production.
Single-purpose agents
Focused agents with a defined role: lead qualifier, support responder, invoice processor, research assistant. One job, done autonomously.
Multi-agent orchestration
Supervisor agents delegating to specialist subagents — for complex, multi-step workflows that require coordination across functions.
Persistent memory agents
Agents that remember context across sessions — user preferences, conversation history, prior decisions — using PostgreSQL or vector memory.
Tool-using agents
Agents with access to real tools: web search, code execution, database reads/writes, API calls, file manipulation — act, not just respond.
Customer-facing agents
Deployed on Telegram, WhatsApp, web chat, or email. Handle inquiries, qualify leads, book appointments, escalate edge cases.
Internal ops agents
Personal assistants for operations teams — managing emails, calendars, research tasks, and cross-system coordination via mobile interface.
Standard RAG pipelines
Document ingestion, chunking, embedding, vector storage, and retrieval — grounding LLM responses in your actual data, not hallucinations.
Advanced / hybrid RAG
HyDE, parent-child chunking, reranking with cross-encoders, multi-query retrieval, and metadata filtering for precision at scale.
LLM fine-tuning
Domain-specific fine-tuning using LoRA, QLoRA, and full fine-tuning on Hugging Face models — for tasks where prompt engineering isn't enough.
Multi-modal RAG
Pipelines that retrieve across text, images, and tables — for product catalogues, technical manuals, and mixed-format knowledge bases.
Knowledge base Q&A systems
Internal wikis, compliance documents, product manuals — turned into queryable systems that answer in seconds with cited sources.
LLM evaluation & monitoring
Automated evaluation of retrieval quality, response faithfulness, and latency — with dashboards and alerting for production systems.
Multi-engine OCR pipelines
Dual-engine routing between Mistral Vision OCR and Google Cloud Vision — optimal model selected per document type, with automatic fallback.
Handwriting recognition
Extraction from handwritten notes, forms, and annotations — including cursive, mixed print/handwrite, and degraded document quality.
Table & structured data extraction
Complex table recognition with merged cells, spanning headers, and nested structures — output as JSON, CSV, or directly into your database.
Invoice & receipt processing
End-to-end extraction from financial documents — vendor details, line items, totals, taxes — structured and validated before writing to your systems.
Image preprocessing pipelines
Blur correction, contrast enhancement, perspective warping, and deskewing — for real-world documents that arrive in non-ideal conditions.
High-volume production systems
Serverless architectures handling thousands of documents daily, with Redis caching, error correction, and sub-second per-document latency.
Custom model training
End-to-end: data collection, preprocessing, architecture design, training, evaluation, and production packaging. PyTorch or TensorFlow.
Predictive analytics systems
Forecasting, anomaly detection, churn prediction, fraud detection — production-deployed models that feed into operational dashboards.
NLP & text classification
Sentiment analysis, intent detection, entity extraction, document classification — using fine-tuned transformers on your domain-specific data.
MLOps & deployment
CI/CD pipelines for ML models. Containerised deployment, model registry, A/B testing infrastructure, and monitoring with drift detection.
Model optimisation
Quantisation, pruning, distillation, and ONNX export — making models faster and cheaper to run in production without losing accuracy.
Data pipelines & feature stores
ETL pipelines, feature engineering, data versioning, and automated retraining triggers — keeping production models current as your data changes.
Every technology we work with
/ What we've shipped
Manufacturing · Computer Vision
Quality control eliminating manual inspection on production lines
Manual insole classification was inconsistent across shifts. A tired inspector at 4pm isn't the same inspector as 9am Monday. Shipping errors were climbing. The client needed machine-consistent accuracy at any hour.
Financial Services · Document AI & OCR
Dual-engine OCR pipeline for impossible document formats
Handwritten forms. Photos of receipts. Tables with merged cells. Every off-the-shelf OCR tool had failed. The client needed one system that handled all of it, at scale, without human review on every document.
Real Estate · AI Agents
24/7 property inquiry agent with zero additional headcount
Hundreds of inquiries per day. First-to-respond wins in real estate. Manual reply at 9am can't compete with an agent that responds at 11pm. Needed full qualification — budget, preferences, viewing schedule — without a human present.
/ The honest comparison
| The Question | Buteforce | Others |
|---|---|---|
| Who trains the models? | ✓ Your data. Your edge cases. | – Plug-in APIs, no customisation |
| What does "production" mean? | ✓ Real error handling, real scale | – Prototype needing a rebuild |
| Who owns the output? | ✓ You own everything | – Locked into their platform |
| How are outcomes defined? | ✓ Specific metrics agreed upfront | – Vague deliverables, scope drift |
| What happens post-delivery? | ✓ 30-day refinement, then yours | – Disappear after launch |
| How deep is model expertise? | ✓ Deep in every model we use | – Resellers of tools they don't fully understand |
/ How it works
One conversation
Tell us the process that costs you the most time. We ask uncomfortable questions. This is where most agencies pitch. We're still listening.
We design the system
Before code, we map data flow, model selection, integration points, and what done looks like. Architecture decisions are permanent — we get them right first.
Production-grade build
Not a prototype. Real error handling, real logging, real performance on your data. Edge cases included. You know when something goes wrong before your clients do.
You own everything
Code, models, infrastructure — all yours. Full documentation. We stay for 30 days of refinement. Then it runs without us. That's the point.
/ What people ask
Because most "AI solutions" are off-the-shelf tools configured to look custom. When they fail, it's because the model was never trained on your data, or the system was a prototype that couldn't handle production volume, or the edge cases were never accounted for. We train on your actual data, agree on success metrics before writing a line of code, and build for the edge cases — not just the happy path.
You could. Recruiting takes 3–6 months. Onboarding another 2–3. A good AI engineer costs £80–120k UK, $140–200k US — before benefits. One engineer brings expertise in one area. We bring production experience across automation, vision, agents, RAG, and OCR simultaneously. For a defined problem you need solved now, we're faster, cheaper, and lower risk.
A focused automation workflow can be live in 2–3 weeks. A custom-trained computer vision system — data collection, training, evaluation, deployment — typically takes 4–6 weeks. A full RAG pipeline or multi-agent system is usually 3–5 weeks. We give you a specific date after one call, not a range.
We agree on measurable success criteria before we start. Not "the system works" — specific accuracy thresholds, processing speeds, automation rates. If the system doesn't hit them, we don't stop until it does. We also stay for 30 days post-launch — that's when real usage surfaces real refinements that no test environment predicts.
Almost certainly. We've integrated with Salesforce, HubSpot, SAP, various ERPs, Gmail, Google Workspace, Telegram, WhatsApp Business, and most things with an API or webhook. If your system can receive data, we can send it. If your system emits events, we can listen to them.
Sometimes yes, sometimes we help collect it. For manufacturing QC, we typically need 500–5,000 labelled images of your specific products. We'll tell you exactly what's needed in the first conversation — we've built data collection pipelines as part of projects before.
/ Two ways in
Book the 30-minute call
We talk. You describe what your team does every day. We identify the highest-leverage automation opportunities and tell you exactly what we'd build — and whether it's worth it. You leave with a clear picture and zero obligation.
- Specific automation recommendations
- Real cost and timeline estimate
- Honest fit assessment
Get the AI Audit framework first
Not ready to jump on a call? Drop your email. We'll send you the exact framework we use to audit a business — the same one we'd work through together on the call.
- The audit framework as a PDF
- A worked example for a startup
- Follow-up Q&A by email if you want it
/ The last step
Pick a time that works. Show up. Walk away with a clear, specific answer.
Not ready? Get the free audit framework