AI and ML wired into existing systems — without rebuilding them.
Most products that say 'AI-powered' are integrations: a model API call wired into existing software. The hard part is doing it without making the product slower, more expensive, or less reliable. We integrate AI and ML models into existing systems without rebuilding them.
The wave of "add AI to everything" experiments from 2023 and 2024 left most engineering teams with the same lesson: getting a demo working is easy, getting it working reliably in production is hard. The technical gap is specific: prototype AI systems have no observability, no graceful degradation, no evaluation framework, and no plan for when the model returns something wrong. These are engineering problems, and they have engineering solutions.
Retrieval-Augmented Generation has become the standard answer to "how do we make this LLM know about our specific data." The pattern is simple in concept: retrieve relevant documents from a vector store, inject them into the model context, generate a grounded response. The implementation complexity is entirely in the details.
Chunking strategy matters more than most teams expect. Too-large chunks reduce retrieval precision. Too-small chunks lose semantic coherence. Overlapping chunks help with boundary cases but increase index size. The right chunking strategy is document-type-specific, and there is no universal answer.
Embeddings have become infrastructure, not a specialized ML technique. Any time you need semantic similarity — document retrieval, duplicate detection, recommendation, classification without labeled data — embeddings are the right tool. The explosion of high-quality embedding models (text-embedding-3-large, voyage-3, jina-embeddings-v3) and the maturation of pgvector means the barrier to deploying embedding-based features is now very low.
The pattern that emerges in 2026: engineers add vector columns to existing Postgres tables, run embedding generation as a background job on document insert/update, and query by cosine similarity as naturally as they query by any other field. No separate vector database required for most applications.
The model serving decision in 2026 has three paths. Managed API (OpenAI, Anthropic, Gemini): correct for most applications — low operational overhead, state-of-the-art models, pay-per-token pricing. Self-hosted with vLLM or TGI: correct when you have data residency requirements, need predictable inference cost at high volume, or require model customization. Fine-tuned on managed infrastructure (Together AI, Fireworks): correct for the narrow cases where prompt engineering is insufficient and you need model behavior change without self-hosted infrastructure complexity.
The most common wrong decision is moving to self-hosted serving to save cost, underestimating the operational complexity, and ending up with a less reliable system that costs more in engineering time than it saves in API fees.
Most AI integration projects ship a prototype, then stall. The notebook works; the API built around it falls over under load, drifts as data changes, and has no monitoring when it starts returning wrong answers. We build the bridge between working demo and production service — with proper error handling, observability, and fallback behavior from day one.
That means RAG pipeline design with real retrieval evaluation, not just cosine similarity and hope. It means choosing between pgvector, Pinecone, or Qdrant based on your actual scale and operational constraints. And it means using LangSmith or Langfuse to answer the question your CTO will ask in month two: why did this return that, and what did it cost?
We define exactly what the AI integration needs to do, what data it operates on, and how its output gets consumed downstream. A precise scope — not 'add AI to this feature' but 'retrieve the top 3 relevant policy sections given a user query and surface them with source attribution' — makes every subsequent architecture decision tractable.
We assess the quality, freshness, and accessibility of the data the AI component will use. For RAG, that means evaluating document structure, update frequency, and retrieval complexity; for ML models, it means confirming that the features available at inference time match what the model was trained on.
We select the integration pattern — RAG, fine-tuning, prompt engineering, or hybrid — and design the serving layer, data pipeline, and feedback loop. Fallback behavior when the AI component fails or returns low-confidence output gets designed here, not as an afterthought.
We build the evaluation framework before we build the integration. For RAG, that's a retrieval evaluation dataset with relevance scoring; for ML models, a held-out test set with baseline metrics. You can't improve what you can't measure, and you can't trust a system you've never benchmarked.
We implement the integration as a production service — proper API surface, error handling, timeout policies, retry logic, and structured logging. The AI component gets the same engineering treatment as any other service your team will have to operate at 2am.
We ship with cost tracking, latency dashboards, and output quality monitoring in place. For LLM integrations we use LangSmith or Langfuse; for traditional ML, custom metric pipelines with alerting on distribution drift so you know before your users do.
Tools and infrastructure we use for this capability.
We treat the model API as one more dependency — with timeouts, retries, fallback paths, and cost ceilings. Your existing system architecture stays intact; AI becomes a service it calls.
Chunking strategy, reranking, embedding model choice, and retrieval evaluation are decisions we make based on your corpus — not defaults copied from a tutorial.
Token usage, latency, cost per request, retrieval hit rate, and grounding accuracy — instrumented before launch so you know when the integration starts drifting.
RAG wins when the information the model needs changes over time — documents, policies, product data — or when you need citations and source attribution, or when your knowledge base is too large for a context window. Fine-tuning wins when you need to change the model's behavior style, adopt domain-specific reasoning patterns, or enforce a constrained output format that prompt engineering alone can't reliably produce. For most enterprise knowledge applications, RAG is the answer.
If you're already on Postgres and your collection is under 10 million rows, pgvector is the right answer — no new infrastructure, no new operational burden. If you need hybrid search (keyword plus semantic), Weaviate handles that well. For pure vector search at high scale with managed infrastructure, Pinecone is the default. Qdrant is worth considering for self-hosted deployments where you need more operational control than pgvector provides.
Grounding is the main lever: put the relevant information in context via RAG rather than relying on the model's parametric memory. Add output validation that checks structural correctness and flags low-confidence responses. Build evaluation pipelines that catch regressions before they reach production — hallucinations typically correlate with specific input patterns that a well-constructed eval dataset will surface.
For slowly changing documents, scheduled full re-indexing works fine. For frequently updated content, you need incremental indexing triggered by document change events — a webhook or change data capture pattern that queues updated documents for re-embedding. The key constraint is never letting stale embeddings serve queries on content that has materially changed.
A focused RAG system — document ingestion pipeline, vector store, retrieval API, and LLM integration — typically runs three to five weeks at $50/hr. More complex integrations involving custom model serving, feature stores, or real-time ML pipelines run longer. We scope based on your existing infrastructure and the specific integration points, not from a template.
Anonymized engagements with real outcomes — no client names per NDA.
34%
Forecast Accuracy Improvement
61%
Stockout Reduction
23%
Overstock Reduction
“The stockout reduction was measurable within the first planning cycle. Category managers were sceptical initially, but the forecast accuracy on the products that had historically been hardest to predict won them over.”
— Head of Supply Chain Planning, FMCG Distributor
41%
Faster Critical Response
91%
Alert Precision
31%
False Alarm Reduction
“The false alarm rate was a genuine patient safety issue — staff were silencing monitors as a coping mechanism. The AI layer changed the signal-to-noise ratio enough that nurses are paying attention to alerts again.”
— ICU Clinical Director, Regional Medical Centre
18%
Fuel Cost Reduction
96.5%
GPS Uptime
14%
Empty Mile Reduction
“The empty mile reduction paid for the system within the first two months of operation. The dispatch team now has real information to make decisions from instead of relying on driver phone calls.”
— Operations Director, Logistics Company
AI/ML Integration sits beneath the services we sell and the agents we ship. If you are scoping outcomes rather than tools, start with one of these.
Tell us what you are building. We will scope it, price it honestly, and give you a clear plan.
Start a ConversationFree 30-minute scoping call. No obligation.
Fordel Studios