We build generative AI, LLM apps, RAG chatbots and AI automation that ship into production, not slide decks.
Most AI projects stall because they chase a demo instead of a task. We start with the one job the AI must do, build a prototype on your real data in the first few weeks, and measure accuracy with an evaluation set before launch. You get a feature that grounds its answers in your data, knows when to say it is unsure, and runs at a cost you can predict. See what a build costs in our custom software development cost guide.
What every AI build includes
AI development is building software that uses large language models and machine learning to do real work: chatbots that answer from your documents, document processing, data extraction and workflow automation. It serves startups adding an AI feature and enterprises automating manual tasks. The differentiator is grounding answers in your data with RAG and measuring accuracy before launch. Timeline Digital builds these features in about 8 to 10 weeks, from $15,000 to $35,000 against a fixed scope, with the full source code yours.
The AI projects that return value are narrow and specific. Here are the four patterns we build most, because they solve a measurable problem instead of chasing a headline.
A support or internal assistant that answers from your help docs, policies or product manuals through RAG, with citations and a clear "I do not know" when the answer is not in the source. It cuts repeat tickets and gives staff one place to ask.
Read invoices, contracts, forms or resumes and pull the fields you need into structured data your systems can use. The model returns validated JSON against a schema, and low-confidence extractions get flagged for a human instead of silently going wrong.
Route, summarise, draft and classify the work your team handles by hand: triaging emails, drafting replies, summarising long threads, tagging records. The AI handles the routine pass and a person reviews the edge cases.
Search that understands intent, content generation, recommendations or an in-app copilot. We build the AI as one part of a real product, with the streaming, rate limits and cost controls a live feature needs.
Four phases. A prototype on your real data early, accuracy measured before launch.
We start with the task, not the model. What question must the AI answer, what documents or data does it read, and what does a correct answer look like. We review the data you have, flag what is missing, and decide whether a plain LLM call, a retrieval pipeline or a fine-tune fits. You approve the approach before any code is written.
We build a working prototype on a slice of your real data and put it in front of you. This is where you see actual answers, spot the wrong ones, and we tune retrieval, prompts and guardrails against examples that matter to your business instead of a demo dataset.
We wire the pipeline into your product or workflow: ingestion, vector search, the model calls, guardrails, logging and a fallback for when the model is unsure. We add evaluation so you can measure accuracy on a fixed test set instead of trusting a vibe.
We run the system against a labelled test set, set thresholds for when a human should review an answer, add cost and latency monitoring, then ship. You get the source code, the prompts, the eval suite and a handover so your team can extend it.
Claude, GPT and open models through one provider-agnostic layer, so you are not locked to a single vendor and can route cheaper requests to smaller models. Model choice is a config value, not a rewrite.
Retrieval augmented generation over your private documents using pgvector, Pinecone or Qdrant. The model answers from your data with citations, instead of guessing from training data it never saw.
Input and output validation, prompt-injection checks, PII redaction and structured output schemas so the model returns clean JSON your code can trust, not free text you have to parse.
LangChain, LlamaIndex or plain typed code for multi-step chains, tool calling and agent workflows. We pick the simplest thing that ships and avoid framework lock-in where the logic is small.
A labelled test set and automated scoring so accuracy is a number you can track across model and prompt changes. Without evaluation, every prompt tweak is a guess.
NestJS on Node.js or ASP.NET Core for the API, Next.js for the interface, streaming responses and a job queue for long-running document processing.
Self-hosted open models or zero-retention API tiers when your data cannot leave your control. We scope the data path before building so compliance is designed in, not bolted on.
Senior engineers who have shipped AI features into production, plus a product owner who keeps the build aimed at the one task that returns value.
Fixed scope, fixed quote on the build. Model API usage is metered separately and monitored.
| Project | What it includes | Typical range | Timeline |
|---|---|---|---|
| AI proof of concept | One use case, prototype on your data, accuracy check | From $3,000 | 2 to 3 weeks |
| Production AI feature | RAG chatbot or document pipeline, guardrails, evaluation | $15,000 to $35,000 | 8 to 10 weeks |
| AI platform build | Multiple workflows, agents, fine-tuning, compliance | $50,000 and up | 3 to 5 months |
Build ranges depend on data volume, accuracy targets and integrations. Model API cost is usually a few cents per request, which we estimate up front.
An AI development company builds software that uses machine learning and large language models to do real work: answering questions over your documents, automating manual tasks, classifying or extracting data, and running chatbots that pull from private data. The work covers data pipelines, the model integration, retrieval, guardrails and evaluation, not just an API call. Timeline Digital builds generative AI and LLM features that ship into production, with the accuracy measured rather than assumed.
RAG, retrieval augmented generation, is how you make an LLM answer from your own data instead of its training. Your documents are split, embedded into a vector database, and the most relevant pieces are pulled into the prompt at query time so the model answers with your facts and cites them. It matters because it cuts hallucination, keeps answers current without retraining, and lets the model work over data it has never seen. Most useful business AI features are a RAG pipeline at their core.
A focused AI feature, such as a chatbot over your documents or a document-processing pipeline, typically runs from $15,000 to $35,000 against a fixed scope. A larger build with multiple workflows, agent tool-calling, fine-tuning or strict compliance runs from $50,000 and up. On top of the build there is an ongoing model API cost, usually a few cents per request, which we estimate and monitor so it does not surprise you. You own the source code on delivery.
A scoped generative AI feature takes about 8 to 10 weeks with a senior team. The first prototype on your real data lands in the first three to four weeks, because seeing actual answers early is what surfaces the wrong ones. The rest of the time goes into retrieval tuning, guardrails, evaluation and wiring it into your product. We agree the scope in writing before starting so the date is real.
A poorly built one will. We reduce hallucination by grounding answers in your data through RAG, requiring citations, validating the output against a schema, and returning a clear "I do not know" instead of a confident wrong answer. We also build an evaluation set so accuracy is measured before launch and after every change. No LLM is perfect, so for high-stakes answers we route low-confidence cases to a human for review.
Yes. We scope the data path before writing code. When your data cannot leave your control, we use self-hosted open models or zero-retention API tiers that do not train on your inputs. We add PII redaction, access controls and audit logging. For regulated work such as fintech or healthcare we design the pipeline around the compliance rules first. See our broader work on custom software development for how this fits a larger system.
Bring us the task and a sample of the data it has to read. We will tell you honestly whether AI is the right tool, agree a fixed scope, and put a prototype on your real data in front of you within the first few weeks. You own the code, the prompts and the eval suite.