Custom AI solutions built on your data
Generic AI knows a lot about the world. It knows nothing about your contracts, your clinical policies, your internal SOPs, or the product documentation sitting in your Confluence instance. That gap is where custom AI earns its keep.
We build retrieval-augmented generation (RAG) pipelines, internal knowledge bases, document processing systems, and AI copilots that operate on your actual data. Most projects ship in 4 to 10 weeks. Pricing starts at $4,000 for a single-domain knowledge base and scales to $30,000+ for multi-system document processing pipelines.
why generic AI fails on your data#
what language models actually know (and what they don't)#
Language models are trained on snapshots of public text. The knowledge has a cutoff date, excludes proprietary information entirely, and cannot reason about documents it has never seen. Ask a generic AI tool about your employee handbook, your product specifications, or your client contract terms. It guesses.
the gap between AI demos and production retrieval#
Connect a language model to a document upload and the demo looks great. Paste in a PDF, ask questions, get answers. What the demo doesn't show is what happens at month three, when the document library has grown from 50 files to 4,000, and queries are coming from users who phrase things differently than the documents were written.
This is the production gap. Retrieval at scale requires deliberate chunking strategy, embedding model selection calibrated to your content type, a vector store architected for your query volume, and a reranking layer that returns contextually relevant results even when initial retrieval is approximate. Most off-the-shelf tools skip one or more of these layers.
why most RAG prototypes break at scale#
Default chunking splits documents at fixed token counts regardless of semantic meaning. Single-stage retrieval without reranking returns results ranked by vector similarity, which correlates with relevance but is not the same thing. No evaluation layer means no empirical accuracy baseline. You're shipping on vibes.
We've seen this pattern enough times to build our process around the failure modes. The happy path takes care of itself.
what custom AI solutions include#
RAG pipelines: your documents, databases, and internal systems as the source of truth#
RAG pipelines intercept a user's question, retrieve the most relevant content from your data sources, and pass that context to the language model before it generates a response. The model answers based on what you gave it, not what it was trained on. We build pipelines that connect to file storage (SharePoint, Google Drive, S3), relational databases, internal wikis, ticketing systems, and custom APIs.
internal knowledge bases and policy Q&A systems#
Operations and HR teams field the same questions hundreds of times a year. An internal policy assistant built on your actual documents answers in seconds, cites the source section, and flags when a policy document is outdated. Ticket deflection rates from these deployments typically run 40-60% for the query categories in scope.
document processing and data extraction pipelines#
Contracts, medical records, regulatory filings, and invoices all contain structured information buried in prose. Extraction pipelines using language models pull specific fields, summarize clauses, and route documents for review based on what they find. These replace workflows that previously required hours of manual analyst time.
AI copilots for HR, legal, compliance, and customer support#
Think of a copilot as relevant information and draft outputs delivered in the context you're already working in. Not a chatbot. A tool embedded in the workflow. Copilots we've shipped reduce average handling time for knowledge-intensive tasks by 30-50% in the workflows where they've been deployed.
automated insight and report generation from unstructured data#
Customer feedback, support transcripts, and field reports contain signal that manual analysis can't fully process at volume. Language model pipelines categorize, extract sentiment, identify anomalies, and produce structured summaries. What would otherwise take days of analyst time gets compressed into hours.
how we build custom AI systems#
step 1: data audit#
Before writing a line of code, we examine your source data. Format distribution, quality (clean text vs scanned images requiring OCR), failure modes (inconsistent document structure, conflicting policy versions). Honestly, this step is tedious. It takes one to two days, and most of it is spreadsheet work. But it saves weeks of rework later. The output is a written assessment of retrieval risk by document category.
step 2: architecture decision#
Based on the audit, we recommend an architecture. RAG works for most knowledge retrieval use cases because it keeps the knowledge base updatable without retraining and provides source attribution on every answer. Fine-tuning applies when you have a narrow, high-volume, deterministic task with enough clean training data. We document the tradeoffs in writing. You make the call.
step 3: retrieval pipeline build#
Chunking strategy is calibrated to your document structure: semantic boundaries, not fixed token counts. Embedding model selection depends on content type and query vocabulary. Vector store selection depends on query volume, latency requirements, and data sensitivity. Reranking adds a second-pass scoring layer using a cross-encoder to improve relevance beyond nearest-neighbor retrieval. Every decision is documented so you're not inheriting a system you can't reason about.
step 4: integration#
If users have to leave their existing workflow to use the retrieval system, they won't use it. We connect completed systems to Slack, Teams, internal web apps, CRM platforms, or custom UI. API-first builds make downstream integration straightforward. We handle authentication, access controls, and data boundary enforcement.
step 5: evaluation and accuracy benchmarking#
No system leaves our hands without an evaluation run against your actual expected query patterns. We measure retrieval recall, answer faithfulness, and answer relevance. Results ship in a written report before handoff. You know the accuracy floor before the system goes live. If any category falls below threshold, we iterate before delivery.
RAG vs fine-tuning: the real decision#
when RAG is the better choice (most of the time)#
RAG is the better architecture when your knowledge base changes frequently, you need source attribution on every answer, or your documents were not available at model training time. It is also significantly cheaper to maintain. You update knowledge by updating documents, no retraining cycle needed.
The RAG market was valued at $1.94 billion in 2025 and is projected to reach $9.86 billion by 2030 at a 38.4% CAGR (MarketsandMarkets, 2025). In active enterprise deployments, RAG is selected for 30-60% of AI use cases (Vectara / Mordor Intelligence, 2025).
when fine-tuning adds value (and when it doesn't justify the cost)#
Fine-tuning makes sense for narrow, high-volume, deterministic tasks with a large, clean training dataset, and when latency is a hard constraint that retrieval context overhead can't accommodate. Fine-tuning a large language model costs $5,000 to $50,000 upfront (Scopic, 2025), not counting ongoing retraining as task definitions evolve. For document retrieval and policy Q&A, that investment rarely pays off compared to a well-engineered RAG system.
why hybrid approaches are increasingly common in production#
In mature deployments, RAG and fine-tuning are not mutually exclusive. One common pattern: a fine-tuned model handles classification and routing at high volume while a RAG layer handles open-ended knowledge queries. You get the strengths of both approaches without paying the full cost of either everywhere.
our technology stack#
Retrieval and embedding: LangChain, LlamaIndex, ChromaDB, pgvector, Pinecone. We pick based on hosting requirements, query volume, and latency targets.
LLM providers: Claude, GPT-4o, Gemini, Mistral. We're model-agnostic by design. For clients with data sovereignty requirements, open-source models deployed on your own infrastructure are the path. See our self hosted AI infrastructure service.
Document processing: Unstructured and Docling for complex document parsing: multi-column PDFs, scanned records, tables and figures that generic loaders handle badly. Custom parsers for document types with non-standard structure.
Infrastructure: Self hosted or cloud, depending on data sensitivity. 70% of companies using generative AI are augmenting base models with retrieval systems rather than relying on public LLMs alone (Databricks State of AI, 2025).
use cases we've built for#
Legal, contract review and clause extraction: RAG systems built on precedent libraries return relevant clauses in seconds, with citations. Extraction pipelines identify non-standard terms automatically for attorney review. See how this connects to multi-step workflows on our agentic AI service page.
Healthcare, clinical policy Q&A and prior authorization support: Policy Q&A systems cut the time clinical staff spend searching for the right procedure, cite the specific policy version, and flag conflicts between documents. Prior authorization support extracts payer criteria and matches against patient data. See our HIPAA-compliant AI guide and self hosted AI service for compliant deployment details.
HR and operations, SOP and policy assistants: Policy assistants answer questions instantly with citations, and route questions they can't answer confidently to the right person. Workflow automation integrations handle routing and update triggers when source documents change. See our workflow automation service.
Customer facing, product knowledge bases: Answers grounded in your actual product documentation reduce tier-1 support ticket volume. Ticket deflection for in-scope query categories is typically measurable within the first 30 days.
pricing#
All projects start with a scoping call and data audit.
Single-domain knowledge base or RAG system, $4,000 - $10,000 One knowledge domain, 100-2,000 documents, one or two data sources. Includes data audit, full retrieval pipeline build, evaluation run, and integration to one target interface. Timeline: 3-5 weeks.
Multi-source document processing pipelines, $12,000 - $30,000+ Multiple knowledge domains, complex document types requiring custom parsers, multi-system integration, or structured extraction pipelines with downstream data delivery. Timeline: 6-10 weeks.
Ongoing optimization and expansion retainers, $1,500 - $4,000 / month Post-launch optimization, evaluation monitoring, knowledge base expansion, model provider updates, and integration maintenance. Most clients on retainer add two to four new knowledge domains per quarter as internal adoption grows.
frequently asked questions#
What is a custom AI solution and how is it different from off-the-shelf AI? Off-the-shelf tools operate on public model knowledge and whatever documents you upload in a given session. A custom solution connects a language model to your specific data sources through a retrieval layer, so every answer is grounded in your documents and workflows.
How much does it cost to build a custom RAG pipeline or AI knowledge base? $4,000 to $10,000 for a single-domain knowledge base. $12,000 to $30,000+ for multi-source pipelines with complex document types and multi-system integrations. The main cost drivers are number of data sources, document complexity, integration requirements, and whether you need self hosted infrastructure.
What tools are used to build custom AI on proprietary business data? LangChain or LlamaIndex for retrieval orchestration. ChromaDB, pgvector, or Pinecone for vector storage depending on hosting requirements. Unstructured or Docling for complex document parsing. LLM providers include Claude, GPT-4o, Gemini, and Mistral, selected based on task requirements and data residency constraints.
When should a business fine-tune an AI model vs use RAG? Start with RAG. It covers the large majority of knowledge retrieval use cases. Fine-tuning makes sense for narrow, high-volume, deterministic tasks with a large training dataset, but it costs $5,000 to $50,000 upfront and you'll retrain as definitions evolve. For most business knowledge applications, RAG delivers better economics and more flexibility.
How long does it take to build a custom AI system for a business? 3-5 weeks for a single-domain knowledge base, 6-10 weeks for multi-source pipelines. The biggest variable is data readiness on your side. The data audit in week one catches access and version conflict issues early.
What does the evaluation process look like before handoff? We build a test set from your actual expected query patterns and measure retrieval recall, answer faithfulness, and answer relevance. Results ship in a written report. You get a documented accuracy baseline before the system goes into production.
Can a custom AI system run on our own infrastructure? Yes. For organizations with data sovereignty requirements, we build on self hosted infrastructure: vector stores, embedding models, and LLMs running in your environment with no data transiting third-party cloud services. See our self hosted AI service and the self hosted vs cloud comparison.
book a technical audit#
If you've tried a RAG prototype that worked in the demo and fell apart in production, the problem was probably the retrieval architecture. We'll review your data, identify where retrieval will break, and give you a clear architecture recommendation before any money changes hands.
If your problem is process automation rather than knowledge retrieval, workflow automation may be the better starting point. If you need a system that takes multi-step actions rather than answers questions, read about agentic AI first.
