Solutions
Generative AI Development Services
Generative AI development services cover the full path from use case to production: RAG systems over your documents, LLM-powered features, autonomous agents, fine-tuning, and integration with your existing stack. We build on Claude, GPT, and open-weight models, with evaluation suites and cost controls — deployed cloud, hybrid, or fully on-premise.
Inside the delivery
For a knowledge-based application, connect source permissions and retrieval to generation, then evaluate the answer before expanding the workflow.
Ingest permitted documents with ownership, version and access metadata.
Output
Searchable, permission-aware content
Find relevant passages and preserve the source information passed to the model.
Output
Task-specific evidence
Generate an answer, validate its structure and handle insufficient evidence.
Output
An answer with inspectable sources
Compare quality, latency and cost using representative questions and failure cases.
Output
Evidence for release or revision
Controls across the workflow
Before you commit
A language or multimodal model can help generate, retrieve or transform information in a defined workflow.
Overview
Programmatic designs generative-AI applications and integrates them into existing products, knowledge systems, workflows, and business platforms with retrieval, permissions, evaluation, guardrails, observability, and human oversight.
Capabilities
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Build focused applications for drafting, summarization, extraction, search, transformation, classification, analysis, and domain workflows.
Add generative AI behind the product's current APIs, identity, permissions, and business rules instead of creating a disconnected prototype.
Ground model responses in governed documents, knowledge bases, data products, and approved sources with permission-aware retrieval.
Create assistants that can reason over context, call approved tools, propose actions, or automate constrained steps with escalation and review.
Select and route models based on quality, latency, cost, privacy, context, and task requirements; adapt behavior only where evidence supports it.
Treat generative AI as an operating system component with repeatable tests, monitoring, cost control, versioning, and failure analysis.
Comparison
| Criteria | Prompt engineering only | RAG (retrieval-augmented) | Fine-tuning |
|---|---|---|---|
| Best for | Simple, general tasks | Answers grounded in your documents & data | Consistent style/format at very high volume |
| Uses your private knowledge | Only what fits in the prompt | Yes — retrieves from your sources at query time | Baked in at training time; goes stale |
| Keeping knowledge current | Manual prompt updates | Automatic — update the source documents | Requires re-training runs |
| Typical build effort | Days | Weeks | Weeks–months + data preparation |
| Hallucination control | Weakest | Strong — answers cite retrieved passages | Moderate |
| When we recommend it | Prototypes, internal utilities | Most business use cases start here | After RAG proves value and volume justifies it |
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Pricing
Scope depends on the first use case, source-data readiness, integration depth and deployment constraints. An evaluated prototype can clarify feasibility before a production estimate. The proposal separates implementation from model usage, hosting and ongoing operations.
2–4 weeks against one measurable target on your real data. You keep the code and the eval results either way.
Fixed-scope production build: architecture, integration, evals, guardrails, deployment, handover docs.
An ongoing team (engineer + reviewer, scaled as needed) iterating on your AI systems monthly. Cancel with 30 days' notice.
Integrations
We select tools around your existing systems, data requirements and operating constraints.
Frequently asked questions
Generative AI is particularly useful for language, content, extraction, transformation, and flexible interaction, while traditional ML may be better for forecasting, classification, ranking, anomaly detection, or other trained predictive tasks. Many systems use both.
Retrieval-augmented generation supplies a model with relevant approved information at request time. Enterprise implementations also need permissions, content lifecycle, source quality, indexing, evaluation, and traceability.
Yes. We normally place the AI layer behind stable interfaces and connect it to existing identity, data, APIs, workflows, and controls rather than requiring a product rebuild.
We constrain the task, provide better source context, use retrieval where appropriate, require structured outputs, validate deterministic fields, design refusal or escalation behavior, and evaluate against representative examples. No model should be treated as infallible.
Fine-tuning can help when a repeated task needs specific behavior or output patterns that prompting and retrieval cannot meet reliably. We recommend it only after an evaluation baseline shows the need.
If the task has one correct answer that a rule or a query could produce, it usually is not. Generative models earn their place on unstructured input, variable phrasing, and drafting work where a reviewer stays in the loop. We would rather tell you that at scoping than after a build.
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