To first production release
Systems running in production
Engineers on the AI practice
Client retention rate
Compliance certifications held
Generative AI development services are the engineering work that turns a foundation model into a system your business can rely on. That covers use case selection, data preparation, model choice, retrieval design, evaluation, integration, and the monitoring that keeps output stable after launch. A prompt wrapper on an API key is not the same thing. The difference shows after the demo.
Twelve capabilities, grouped by what stage of the system they belong to. Our generative AI software services are scoped individually, so you buy what the use case needs.
Retrieval-augmented generation (RAG) grounds model output in your own documents at query time. Our RAG application development services covers chunking, hybrid search, reranking, and citation tracking, so every answer traces to a source. This is the default fix for accuracy, not fine-tuning.
Fine-tuning changes how a large language model (LLM) formats and behaves, reliably. It is a poor way to inject facts. We fine-tune where prompting plateaus and volume justifies the training and evaluation cost, and we say so when it does not.
Agents call tools, hold state, and act. We scope permissions, define stopping conditions, and route consequential actions through human approval. Our AI agent development solutions starts from the same test.
Multiple agents coordinating on one workflow need a controller, a shared state model, and failure handling per agent. We build the coordination layer, not just the agents. Many projects that reach for this could start with a scripted workflow instead.
Copilots sit inside the tools your team already uses, grounded in your knowledge base. We build the retrieval layer, the permission scoping, and the interface hooks. The engineering difficulty is data access, not the model.
Extraction, classification, and summarization across contracts, claims, invoices, and reports. Pipelines return validated structured output your downstream services can consume, not free text you then have to parse.
Internal search and question answering over policies, wikis, tickets, and product documentation. Retrieval quality depends on how your content is structured, so we assess source condition before quoting a build.
Structured prompts, output schemas, and few-shot patterns, versioned and tested like code. We treat prompts as artifacts under regression control, because an untested prompt change is an untested production change.
We write the test set before the build. Input filtering, output validation, refusal handling, and a regression suite that runs on every prompt or model change. Systems without one degrade silently after launch.
Latency budgets per component, caching, batching, model routing, and quantization where self-hosting applies. Real-time systems cannot absorb an unbounded model call in the critical path.
Machine learning operations (MLOps) covers versioning, rollout, rollback, and environment parity. We ship with a documented path for reindexing and retraining, so your team can run the system without us.
Request-level logging for latency, token spend, retrieval hit quality, and output scoring. When a provider updates a model version, we re-run the evaluation set and compare before promoting the change.
| Foundation Models | Orchestration Frameworks | Vector Databases and Embeddings | Training and Fine-Tuning | Cloud and Infrastructure | Observability and Governance |
|---|---|---|---|---|---|
| GPT-5.x | LangChain | Pinecone | PyTorch | AWS Bedrock | MLflow |
| Claude Opus 5 | LlamaIndex | Weaviate | Hugging Face Transformers | Azure OpenAI | Weights and Biases |
| Gemini 3.x | LangGraph | Qdrant | PEFT, LoRA | Vertex AI | LangSmith |
| Llama 4 | DSPy | Milvus, pgvector | TRL, DeepSpeed | SageMaker | Arize |
| Mistral Large | Semantic Kernel | Elasticsearch hybrid search | Axolotl, Unsloth | Kubernetes | Langfuse |
| Qwen, DeepSeek, Grok | Haystack, CrewAI, Model Context Protocol | Cohere Rerank, OpenAI embeddings | — | Ray, vLLM, Triton | OpenTelemetry, Guardrails AI, audit logging |
These are the generative AI development solutions we build most often, each scoped as a production system rather than a pilot.
Grounded in your help center and ticket history, with escalation paths and human approval on account actions.
Question answering across wikis, policies, and documentation, scoped to each user's existing permissions.
Clause extraction, obligation tracking, and deviation flagging against your own playbook, returned as structured output.
Natural language querying over your warehouse, with generated SQL shown to the user before execution.
Templated and brand-constrained generation with review gates, versioning, and rollback.
Repository-aware assistants for internal codebases, retrieving your own conventions rather than public training data.
Speech-to-text, model reasoning, and text-to-speech within a fixed latency budget, built for real-time handling.
Training and test data where real records are restricted, with distribution checks against the source.
Multi-step processes where the model handles judgment and deterministic code handles everything else.
We front-load the decisions that determine whether a build succeeds, before any code is written. As a generative AI consulting company, we would rather end a project at step one than at month six.
We test whether generative AI is the right tool. Some problems belong to a rules engine, and we say so.
We map where ground truth lives and what makes it retrievable. This step consumes more time than clients expect.
We write the test set first, then build against it. Quality becomes measurable rather than subjective.
Scoped access controls, minimum API permissions, audit trails, and human approval before consequential actions, handled through our AI integration practice.
Request-level logging, drift detection, and a documented path for reindexing and model migration. Our generative AI consulting services can also run standalone, ending in an architecture recommendation you are free to build elsewhere.
Choose a generative AI development company based on what it shows you before the contract, not what it claims after.
We build systems that are auditable and reviewable. We do not certify your regulatory compliance, and no vendor honestly can.
Before we commit to a stack, we assess four areas that determine whether the solution will work in production.
Fit: Business objective and data readiness. We check whether your source content is retrievable in its current state, and what it costs to make it so.
Risk: Security posture and regulatory exposure. We map what data the model can reach, who authorized it, and what the audit record captures.
Cost: Latency, throughput, and inference economics. We budget latency per component and model cost per request at expected volume, not at pilot volume.
Proof: Benchmarked against your evaluation set. We score candidate models on your data, not on public leaderboards, before selection is final.
In-House Team |
Freelance / Contract |
Our Team |
|
|---|---|---|---|
| Time to first release | 6-12 months including hiring | Varies by availability | 6-12 weeks |
| Model and infra expertise | Must be hired or trained | Depends on the individual | Dedicated team, no outsourcing |
| Compliance ownership | Yours | Unclear, often unassigned | Shared, documented in scope |
| Ongoing maintenance | Yours, ongoing headcount | Ends with the contract | Managed operations, optional |
| Scaling risk | Key-person dependency | High turnover | Team continuity, documented handover |
Generative AI development services cover use case qualification, data preparation, model selection, retrieval design, evaluation, integration, and monitoring. The goal is a production system, not a working demo.
Cost depends on data condition, integration depth, and accuracy requirement. Published market figures put a focused startup build on a hosted model at roughly $30,000 to $90,000. Ask any vendor what drives their number.
Focused use cases reach production in 6 to 12 weeks with TrueAICode. Timelines extend when source data needs heavy cleanup or integration touches systems with limited API access.
We use dedicated developers with no subcontracting, write the evaluation set before the build, and run weekly sprint reviews. You see working software, not status decks.
Yes. We are the AI engineering arm of TRUEiGTECH, whose platforms run under high transaction volumes and licensing oversight. Audit trails and approval steps are architecture decisions, not additions.
Access to representative data, a named technical owner, and agreement on what correct output looks like. The third item causes the most delay in practice.
Retrieval grounds answers in your sources with citations. Output validation, refusal handling, and a regression test suite catch failures before release and after every model change.
Access controls scoped to the requesting user, minimum API permissions, audit trails on every request and response, and human approval before any consequential action.
We re-run your evaluation set as a regression suite and compare output quality before promoting the change. Systems built without an evaluation set degrade silently.
When the task has one computable correct answer, when plausible errors go unreviewed, or when source data is too disorganized for retrieval. We say so during discovery.