[00] Compare
Weigh up your options before you commit
Plain-English comparisons of the choices businesses face when planning AI: the criteria side by side, when to choose each and our honest take. We work with every option here, so we have no reason to steer you.
take.log3 decisions · our verdict
- c.01use bothAI agents vs RPA: Use both: RPA for the predictable steps, an AI agent for the parts that need reading and judgement.
- c.02start with off-the-shelfCustom AI vs Off-the-shelf: Start with off-the-shelf tools, and build custom when the value depends on your own data, systems or rules.
- c.03start with ragRAG vs Fine-tuning: Start with RAG and good prompts, and add fine-tuning when an evaluation set shows where they fall short.
from “our honest take” on each comparison
comparisons
Three decisions. Side by side, criteria by criteria.
Each one shows the signals that point to each option and where we would start. Open it for the full criteria table, when to choose each and the questions buyers ask.
c.01 09 criteria
AI agents vs RPA
Which should automate your work?
Rule-following bots and reasoning agents compared, so you can match each process to the right kind of automation.
opt.ause both
AI agents
A language model that reads, decides and uses tools to complete a task
choose it when
- Inputs arrive as emails, documents or chat messages that vary
- The task needs reading, judgement or summarising
- There are too many variations to write rules for each one
opt.buse both
Robotic process automation (RPA)
Bots that follow fixed, rule-based steps across your systems
choose it when
- The steps are the same every time
- Inputs are structured and predictable
- Volumes are high and cost per task matters
our takeUse RPA for the predictable parts and AI agents for the parts that need reading and judgement. A common pattern is an agent that reads an incoming email or document, extracts and checks the details, and then hands structured data to an RPA bot or API to post into your systems.
c.02 10 criteria
Custom AI vs Off-the-shelf
Which should you choose?
Cost, speed, fit, data control and ownership compared, so you know whether to subscribe to an AI tool or build your own.
opt.a
Custom AI solution
AI built around your data, systems and workflow, which you own
choose it when
- The AI must use your own documents, records or systems to be useful
- Data must stay in your cloud account or a specific country
- You need the AI to take actions in your ERP, CRM or internal tools
opt.bstart here
Off-the-shelf AI tool
A ready-made AI product you subscribe to and configure
choose it when
- The task is general, such as drafting, summarising or meeting notes
- You want to try AI quickly before committing budget
- A tool already exists that matches your process closely
our takeStart with off-the-shelf tools for general productivity, and use them to learn where AI helps your team. Build custom when the value depends on your own data, systems or rules, or when data must stay under your control.
c.03 09 criteria
RAG vs Fine-tuning
How to make an LLM work with your data
Two ways to make a language model useful on your data, compared on cost, freshness, accuracy and effort.
opt.astart here
Retrieval-augmented generation (RAG)
The model looks up your documents for every answer
choose it when
- Answers must come from your documents, policies or knowledge base
- Information changes often
- Users need to see sources and check answers
opt.b
Fine-tuning
The model is trained further on your examples
choose it when
- You need a consistent format, tone or classification at scale
- Prompting with examples is not reliable enough
- You have a large set of good, checked examples
our takeStart with RAG and good prompts. For most business assistants, knowledge search and document tools, it gives better factual accuracy, citations and easier upkeep, at lower cost.
how we compare
Honest by design. Four rules every comparison follows.
- 01
We build with every option
Each choice on this page is something we deliver, and often several sit in the same system, so we gain nothing by steering you to one side.
- 02
The same criteria for both sides
Cost, speed, accuracy, data needs and upkeep are scored for each option in plain English, including where our own preferred approach falls short.
- 03
We say when the answer is both
Mixed set-ups are common: an AI agent in front of RPA bots, retrieval feeding a fine-tuned model, custom software on top of commercial models.
- 04
Decide on your data, not opinion
When the choice is close, we score the options on an evaluation set of your real examples for accuracy, cost and speed before anything is built.
still deciding?
Free tools that help, no sign-up.
- AI project cost estimatorGet an indicative budget range for a chatbot, predictive model, AI agent or larger platform, based on our published figures.1 minute
- AI automation ROI calculatorEstimate the hours and money AI could save on a repetitive task, net of review time and running costs, using your own numbers.2 minutes
- AI readiness checklistTwelve questions on use case, data, people and governance that show whether you are ready for an AI project and what to fix first.3 minutes
FAQ
Questions behind these decisions
The questions buyers ask most on each comparison. Can't find yours?
Email sales@appsin.ai or call +91-9025555584.
Ask us directlyWill AI agents replace RPA?
Not entirely. Rule-based automation is cheaper and more predictable for fixed steps. Agents extend automation to tasks RPA could not handle, and the two work well together.
Read AI agents vs RPAHow do you stop an AI agent doing something wrong?
We limit which tools and data each agent can use, validate its outputs, set spending and action limits, require human approval for important actions and log every step. We test it against real cases before it goes live.
Read AI agents vs RPAIs a custom AI solution built from scratch?
Rarely. We usually build on existing models from OpenAI, Anthropic, Google or open-source providers, and develop the parts specific to you: data pipelines, retrieval, prompts, integrations, guardrails and the user interface.
Read Custom AI vs Off-the-shelfCan we start with a tool and move to custom later?
Yes, and it is often the sensible path. Using a tool first shows which tasks matter and gives you real examples to build an evaluation set for a custom solution.
Read Custom AI vs Off-the-shelfDoes fine-tuning teach the model our company knowledge?
Partly, but not reliably. Fine-tuning is better at teaching behaviour than facts, and the model cannot cite where knowledge came from. For factual answers from your documents, RAG is usually the better tool.
Read RAG vs Fine-tuningHow do you measure whether RAG or fine-tuning works?
We build an evaluation set of real questions with expected answers, agreed with your team, and score each approach against it for accuracy, faithfulness to sources and format before anything goes live.
Read RAG vs Fine-tuningFree consultation
Not sure which option fits? Ask us
Describe what you want AI to do, the data you have and the systems involved. We will tell you which option we would start with and why, with no obligation.