◆ Blog
Notes on building AI that ships.
Practical, production-focused writing on RAG, agents, OCR and automation, from real delivered work.
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AML compliance software: what it covers and what it costs
AML compliance software spans screening, monitoring, case management and reporting. Which parts to buy, which to build, and what it really costs.
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AML transaction monitoring: alerts, thresholds and analyst cost
AML transaction monitoring generates alerts your team has to clear. How rules, thresholds and triage decide headcount, and what to measure before buying.
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Clinical documentation improvement: what CDI programmes change
What clinical documentation improvement programmes do, why query volume is the wrong success metric, and where ambient capture changes the CDI workload.
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Conversational AI for customer service: the architecture that holds
What conversational AI for customer service needs underneath it: retrieval with permissions, safe actions, handoff, and the failure modes to design for.
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Document fraud detection: what the pixels tell you
Extraction accuracy will not catch a forgery. What document fraud detection actually checks, why image-level signals matter most, and how to set thresholds.
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Invoice OCR software: choosing one that clears the queue
Invoice OCR software is sold on accuracy and bought on demos. The figure that decides its value is the share of invoices posting with no person involved.
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RAG access control: permissions that survive retrieval
Post-filtering leaks. Why RAG access control belongs in the query rather than the response, what it means for ingestion, and how to audit what a model saw.
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RAG for contract analysis: what retrieval gets right and wrong
RAG for contract analysis breaks on naive retrieval: definitions live elsewhere, absence matters, amendments override. What to build, and how to measure it.
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AI agents for analytics: what works and what misleads
Where AI agents for analytics genuinely help, why natural-language querying is harder than it demos, and the guardrails that stop a confident wrong number.
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AI consulting for small businesses: what is worth paying for
What AI consulting for small businesses should deliver, what to configure rather than build, and the budget at which custom work starts to make sense.
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AI development agency: how to pick one that ships
How to choose an AI development agency on production evidence, what the good ones do differently, and the questions that sort builders from demo-makers.
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AI software development solutions: what you are actually buying
What AI software development solutions include beyond a model, how to compare providers on engineering evidence, and where these projects usually go wrong.
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Chatbot consulting services: what they should deliver
What chatbot consulting services should cover, when a platform is the right answer, and the diagnostic that tells you whether a build is justified.
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Custom AI model development: when training is actually the answer
When custom AI model development beats prompting or fine-tuning, what it costs, and the data volume you need before the arithmetic works at all.
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Digital process automation: what it is and where it pays
What digital process automation covers, how it differs from RPA and workflow tools, and the arithmetic that decides whether a process is worth automating.
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Enterprise chatbot solutions: permissions, actions and boundaries
What separates enterprise chatbot solutions from a support bot: per-user permissions, audit trails, systems of record, and deployment inside your boundary.
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Generative AI vs conversational AI: the difference that matters
Generative AI vs conversational AI explained: what each term covers, where they overlap, and which one your project actually needs.
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HIPAA compliant LLM: what it takes in practice
What makes a HIPAA compliant LLM deployment: the PHI boundary, business associate agreements, retention, de-identification limits and audit requirements.
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Is ChatGPT an LLM? The difference that matters commercially
Is ChatGPT an LLM? It is a product built on one, not the LLM itself. What the distinction means when choosing what to build on and what to buy.
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LLM consulting: what to buy and what to skip
What LLM consulting should deliver, when a short diagnostic beats a build, and how to judge an LLM company on engineering rather than positioning.
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LLM use cases that pay for themselves
The LLM use cases and applications that hold up in production, the ones that do not, and the test for telling them apart before committing budget.
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OCR data entry: replacing typing with extraction
How OCR data entry automation actually works, the accuracy that matters, and the straight-through rate that decides whether replacing manual typing pays.
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RPA for business: where it still fits, and where AI replaced it
Where RPA for business still earns its place, why RPA programmes plateau, and which work moved to AI-led automation instead.
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Software test automation services, and where AI actually helps
What software test automation services cover, which parts AI genuinely improves, and how testing changes when the system under test is probabilistic.
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AI agent use cases that survive production
The AI agent use cases that work in production, the ones that do not, and the test for telling them apart before you commit budget to a build.
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Automated invoice processing: what it costs and what breaks
How automated invoice processing works stage by stage, the straight-through rate that decides its value, and where invoice automation software stops being enough.
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Chatbot examples: what they do and what they cost
Chatbot examples across support, internal knowledge, sales and operations, what each actually does, and which chatbot ideas are worth building rather than buying.
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Custom AI solutions: when building beats buying
When custom AI solutions earn their cost against an off-the-shelf product, the four conditions that decide it, and what custom AI development actually involves.
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Custom EHR development: build, extend or integrate
When custom EHR development beats configuring Epic or Cerner, what interoperability really costs, and the compliance work nobody quotes for up front.
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Enterprise AI agents: identity, audit and cost control
What separates enterprise AI agents from a working demo: identity-scoped permissions, audit trails, cost ceilings, and integration against systems that disagree.
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Healthcare AI consulting: what to expect and what to ask
What healthcare AI consulting should deliver, the questions that separate clinical experience from general AI work, and where these projects usually stall.
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KYC AML software: what to look for, and when to build
How to evaluate KYC AML software on screening quality, false positives and coverage, and the four conditions under which building the pipeline wins instead.
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The KYC onboarding process, step by step
The KYC onboarding process in five stages, where applicants actually drop out, and how to decide between KYC software and building the pipeline yourself.
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LLM testing: how to know a change actually helped
LLM testing needs evaluation sets, not unit tests. How to build one, what to measure, and how to catch regressions before your users do.
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Low code process automation, and where it stops
Where low code process automation genuinely wins, the three things that force a custom build, and the migration cost nobody prices at the start.
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Top AI consulting firms: how to judge them on engineering, not decks
How to evaluate top AI consulting firms on production evidence rather than positioning: the questions, the categories, and what each kind of firm is actually for.
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What is process automation, and what actually automates well
What is process automation: the three kinds, which work automates well and which does not, and where AI changed the answer. With the arithmetic.
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AI automation agency: what they actually do, and how to pick one
An AI automation agency can be a three-person Zapier shop or a team shipping production systems. How to tell them apart before you sign anything.
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AI document management workflow: from filing cabinet to acted-on data
An AI document management workflow changes what the system understands about a document, which changes which workflows are worth automating at all.
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DAX Copilot explained: what it is, what it does, and the rename to Dragon Copilot
Nuance DAX Copilot became Dragon Copilot in March 2025. What the Dragon Ambient eXperience does, how it differs from DAX dictation, and where it stops.
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Epic AI scribe integration: what it actually takes to write into the chart
Epic AI scribe integration is an integration project, not a model problem. The routes in, what each costs, and where builds stall.
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On-premise RAG: building retrieval that never leaves your network
On premise RAG is decided before anything else when documents cannot reach a third party. Self-hosted models, the hybrid middle ground, and what each costs.
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RAG consulting: what to ask for when your system already exists
Most RAG that disappoints fails at retrieval, not generation. What a short RAG consulting engagement should establish before anyone proposes a rebuild.
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RAG for life sciences: provenance, versioning and validated retrieval
RAG for life sciences needs per-claim provenance and reproducible answers. What changes when an answer may reach a regulatory submission.
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Text classification techniques for document AI: what still works
Text classification for document AI: classical classifiers, fine-tuned transformers and zero-shot LLMs each win somewhere. How to pick between them.
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Agentic RAG: self-RAG, corrective RAG and when the loop is worth it
Agentic RAG makes retrieval part of the reasoning loop, not a fixed first step. The patterns that work, what they cost, and when plain retrieval wins.
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Multimodal RAG: retrieval over PDFs, tables and diagrams
Text-only pipelines silently drop the tables and figures where the answer lives. How multimodal RAG indexes visual content, and when page images beat parsing.
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RAG chunking strategies: semantic, fixed and structure-aware compared
RAG chunking strategies set the ceiling on retrieval quality, and most teams tune the model instead. Fixed, recursive, semantic and structure-aware compared.
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RAG evaluation: metrics, Ragas and the golden set you need first
Without RAG evaluation every change is an opinion. The metrics that separate retrieval failure from generation failure, and how to build the golden set.
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RAG vs CAG: when to stop retrieving and cache the whole corpus
RAG vs CAG: cache-augmented generation preloads the corpus and skips retrieval. Where that wins on latency, where it breaks, and how to route between them.
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Agentic AI vs generative AI: the difference that actually matters
Agentic AI vs generative AI: one produces output, the other decides and acts across steps. The distinction changes what the engineering costs.
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AI agent platforms vs custom builds: the four real options
AI agent platforms vs custom builds: who supplies the harness and who supplies the deployment. Agent builders, SDK harnesses and managed agents compared.
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Buying business process automation: what to look for and what to avoid
EY puts initial RPA failure at 30-50%, and 70% of programmes plateau below 50 bots. How to evaluate a provider and scope a programme that survives contact.
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Chatbot development frameworks: what to use in 2026
Eleven chatbot development frameworks compared: Rasa, Dialogflow CX, Lex, Bot Framework, LangGraph and more. Which family to pick, and when to skip both.
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Conversational AI design: the principles that survive production
What conversational AI design means once the model writes the words: boundaries, grounding, repair paths and handoff. With a worked example of each.
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Customer service chatbots: containment, deflection and what actually resolves
Customer service chatbot metrics: Gartner puts RAG containment at 40-65% and median deflection at 22% against vendor promises of 50%. What to buy on.
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Document workflow automation: what it costs and what it actually saves
Document workflow automation replaces handling that costs $5-25 per document. Vendors quote 200-400% ROI. Build the case from your own numbers.
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Choosing an embedding model for RAG: what MTEB does not tell you
Choosing embedding models for RAG: MTEB is a prior, not an oracle. Why leaderboard rank rarely survives your corpus, and the four constraints that decide.
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Google Document AI vs a custom IDP pipeline: how to choose
Google Document AI vs custom extraction: $1.50 per 1,000 pages for OCR, $30 for custom. Where managed processors win and where they stop.
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IT process automation: which tickets actually automate, and which do not
IT process automation: service desk tickets cost $15-22 each and Gartner puts deflection at 20-30% against vendor claims of 50-75%. Which L1 automates.
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Medical dictation software: Dragon, ambient scribes and when to build
Dragon Medical One runs $79-99 per user monthly plus implementation. Ambient scribes error at 1-3% against 7-11% for dictation ASR. Which one you actually need.
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No-code automation platforms vs custom builds: where the line actually falls
No code automation vs custom: Zapier bills per task, n8n per execution. A 10-step workflow at 10k runs is 100,000 tasks. Where custom crosses over.
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Virtual scribe services vs an AI virtual scribe: which fits your practice
Virtual scribe vs AI scribe: human scribes run $1,200-4,000 per provider monthly, an AI virtual scribe $39-700. Where the 10x gap is justified.
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AI chatbot development guide: cost, timeline and what to actually build
An AI chatbot development guide: the three shapes a chatbot can take, what drives cost, realistic timelines, and when custom beats a platform.
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What AI medical scribes actually cost, and when building your own gets cheaper
What ambient AI scribes cost per clinician, why per-seat pricing compounds, and the headcount at which building your own becomes the cheaper option.
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Ambient clinical intelligence: what ambient listening AI actually does
What ambient AI in healthcare actually does: passive capture, ambient speech recognition, and where the real engineering difficulty sits after the transcript.
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Enterprise workflow automation: what breaks at scale and how to design for it
Enterprise workflow automation is mostly reconciliation between systems that disagree. State modelling, idempotency, partial failure and exception lanes.
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Intelligent document automation: from extraction to the action that follows
Intelligent document automation is the full loop: classify, extract, validate, then act. Why projects that stop at extraction just hand the business a CSV.
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RAG as a service vs custom builds: which one you actually need
When a hosted RAG product is the right call and when it stops being one. Permissions, retrieval logic, per-query pricing and the three constraints that decide.
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SOAP notes from a smartwatch: what actually works for ambient capture
Whether SOAP notes from a smartwatch are viable: what the capture pipeline needs to produce a usable note, and which setups actually hold up.
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HIPAA-compliant AI architecture: PHI boundaries, retention and audit trails
HIPAA compliant AI architecture for PHI: where the boundary sits, what a BAA changes about model choice, and why vector stores break retention policy.
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LLM observability in production: traces, evals and regression gates
LLM observability in production: what to instrument so you can debug, prove improvement and catch regressions. Span tracing, eval sets and CI gates.
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LangGraph agents in production: checkpointing, cost ceilings and fallbacks
What separates LangGraph agents in production from ones that only demo well: durable checkpointing, token ceilings, bounded recursion and fallbacks.
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Build vs buy an AI medical scribe: an honest comparison
When a custom AI medical scribe beats Abridge, Nuance DAX or Suki, and when it does not. Cost, timelines, form coverage, EMR integration and residency.
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KYC verification: build vs buy, compared against Sumsub, AU10TIX and Onfido
Build vs buy KYC verification: when your own identity pipeline beats a vendor and when it does not. Pricing, coverage, residency and thresholds compared.
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AI clinical documentation: AI-generated clinical notes, training and risk
AI clinical documentation explained: how AI-generated clinical notes work, the grounding requirements, and how to train clinicians before it reaches a chart.
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Intelligent document processing: OCR, IDP and document intelligence explained
What intelligent document processing involves, how IDP differs from OCR, where accuracy is really won, and how to scope a project that survives production.
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Enterprise RAG architecture: permissions, freshness and evaluation
Enterprise RAG architecture versus a demo: entitlement-aware retrieval, incremental ingestion, hybrid search and an evaluation harness.
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What is an AI medical scribe? A clinician's guide
What is AI medical scribe software, how ambient scribing works, the benefits and limits, and how to choose one you can trust in real care.
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AI for medical notes: how it works, what it saves, and what to watch
A guide to AI for medical notes: the pipeline stage by stage, what it saves clinicians, the risks to manage, and how to tell a real system from a demo.
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AI medical scribe notes: a worked SOAP example
An AI medical scribe note example: a patient-doctor conversation turned into a structured SOAP note, and how speech maps to charting fields.
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How to reduce LLM costs in production
How to reduce LLM costs in production without hurting quality: model routing, caching, retrieval, and smaller models where they genuinely fit.
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LangGraph vs LangChain: which should you use?
LangGraph vs LangChain: not competitors. How they fit together, and when to reach for a graph model over a simple chain.
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How to choose an AI development company
A checklist for hiring an AI development partner: what to look for, the red flags that predict a failed project, and the questions that separate builders.
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Building an AI medical scribe: the pipeline, stage by stage
Building an AI medical scribe: turning a consultation into structured clinical documentation with speech-to-text, entity extraction and confidence scoring.
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Knowledge graphs vs vector search for RAG
Knowledge graphs vs vector search: one finds similar text, the other captures relationships. When each wins, and why the best retrieval uses both.
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What is agentic AI? A practical guide for businesses
What is agentic AI, without the hype: how it differs from a chatbot, where it creates real value, and how to start on it safely.
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How much does it cost to build an AI agent?
A practical breakdown of what actually drives the cost of building a production AI agent, why cheap prototypes fail, and how to budget for something that ships.
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RAG vs fine-tuning: which do you actually need?
RAG vs fine-tuning solve different problems. A decision framework for choosing, and why teams reach for fine-tuning when they actually need retrieval.
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Multi-agent systems with LangGraph: when you need them (and when you don't)
Multi agent systems LangGraph makes easy are also easy to over-use. How to tell when one is the right call, and how to build it so it survives.
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OCR for KYC: building document verification that actually works
What it takes to build automated identity verification with OCR and face matching, from field accuracy to edge deployment, on a system that saved EUR 40k.
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How to stop RAG hallucinations
How to stop RAG hallucinations: most are a retrieval problem, not a model problem. Build retrieval that cites its sources and refuses to invent.
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