From First Prototype to Production,and Every Version After.
Our forward-deployed engineers and domain experts design, build, evaluate and deploy your agents and models on ThinkStack, then keep them accurate as your data, rules and models change. Fine-tuning, transfer learning and vision models are used where they pay back, and every release passes tests your experts graded.
- ELEngagement lead
- FEForward-deployed engineers
- DEDomain expert
- ELEvaluation lead
- Agents and models in production
- An expert-graded test set and release gates
- Deployment in your environment
- A monitoring and retraining plan
Agents Break When Nobody Owns the Lifecycle
Data, rules and models change every month. Without tests, monitoring and retraining, a good first version quietly gets worse.
of AI projects fail, about twice the rate of IT projects without AI.
How we fix itEvery release passes tests your experts graded, from the first version on.
Source: RAND Corporation, 2024of enterprises will demote or decommission production agents after incidents expose governance gaps.
How we fix itGuardrails, tracing and rollback are built during development, not added at launch.
Source: Gartner, May 2026better agentic AI accuracy for organizations that prioritize semantics in AI-ready data, Gartner predicts.
How we fix itWe build the retrieval, terms and data lineage around the model, not just the prompt.
Source: Gartner, 2025The Whole Lifecycle, Not Just the Model
The model is a small part of a production AI system. We build everything around it that makes it work at scale: data, retrieval, tools, tests, deployment and monitoring.
Agent and Workflow Design
Steps, tools, approvals and fallbacks designed with the people who do the work today.
Retrieval and Grounding
Knowledge bases built from your documents, with citations and access that follows your identity rules.
Fine-Tuning and Transfer Learning
When prompting is not enough, we adapt existing models to your domain with hundreds of examples, not hundreds of thousands.
Vision, OCR and Document Models
Models for scanned, handwritten and table-heavy documents, like the lab logs and bioanalytical studies we process for the FDA.
Evaluation and Release Gates
Test sets graded by your experts, judges calibrated to your reviewers, and thresholds every version must pass.
Monitoring and Retraining
Quality, drift and cost tracked in production, with retraining and re-testing when results slip.
Design, Build, Prove, Deploy, Improve
- Weeks 1–21
Design
Process mapped, success measures agreed, and test cases collected from your experts.
- Weeks 2–62
Build
Agents, retrieval, tools and any custom models built and run on real cases every week.
- Weeks 5–83
Evaluate
Quality, grounding, safety and cost scored against your thresholds, with reviewer sign-off.
- Week 8 on4
Deploy and Improve
Promoted through approval gates, then monitored, retrained and re-tested.
Estimate What One Production Agent Returns
Set the volume and effort for the work the agent will take on.
- Baseline measured in week one, from your systems
- Hours, cycle time, cost and quality reported monthly
- Every assumption written down and signed off
What You Get
Everything is versioned in your workspace, so your team can read it, change it and rerun it.
Delivered with every production release
- Agent and workflow specifications, versioned in your workspace
- Knowledge bases built and synced from your sources
- Custom or fine-tuned models where they beat prompting, with model cards
- An expert-graded test set and calibrated automated judges
- Release gate results for quality, grounding, safety and cost
- Deployment to your cloud, GovCloud, on-premises or air-gapped environment
- A runbook for monitoring, retraining and rollback
Engineering Standards We Hold To
The same standards apply to the first version and the fiftieth.
No Release Without Tests
Every version runs the same graded cases, so regressions are caught before users see them.
The Right Model for Each Step
Models are chosen on measured quality and cost, and stay swappable as better ones ship.
Guardrails From Day One
PII, data loss and injection policies are on in development, not added at launch.
Every Change Recorded
Prompts, models and data versions are tracked, so any result can be reproduced.
Your Data Trains Only Your Models
Data used for fine-tuning stays in your environment and is never used for another customer or provider.
You Own the Result
Specs, models, test sets and runbooks stay with you if we part ways.
Related Modules
The platform modules these solutions are built on.
Other Services
Frequently Asked Questions
Common questions about ThinkTrends AI Lifecycle Development.
What is AI lifecycle development?
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AI lifecycle development covers everything needed to take an AI agent or model from idea to production and keep it working: design, data and retrieval, model selection or fine-tuning, evaluation, deployment, monitoring and retraining. ThinkTrends delivers all of it on the ThinkStack platform.
Why do AI agents fail after they reach production?
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AI agents fail in production when data, rules or models change and nobody retests them. Gartner forecasts that 40% of enterprises will demote or decommission production agents after incidents expose governance gaps. ThinkTrends AI Lifecycle Development reruns expert-graded tests on every change, monitors quality and drift, and retrains when results slip.
When should we fine-tune a model instead of prompting?
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Fine-tune when a well-designed prompt with good retrieval still misses your quality bar, when outputs must follow strict formats or terminology, or when a smaller model can match quality at lower cost. We test both on your graded cases and choose on the numbers.
How long does it take to build a production AI agent?
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A typical first agent reaches production in about ten weeks: two weeks of design, four to five of building, overlapping evaluation, and launch through approval gates. Later agents are faster because skills, tools and test sets are reused.
What happened to ThinkTrends Model Lifecycle Management?
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It is now AI Lifecycle Development. The service still covers model building, transfer learning, scaling and resource monitoring, and now also covers agents, retrieval, evaluation and governed deployment.
Where do the agents and models run?
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In your environment: your cloud tenant, AWS GovCloud, on-premises or an air-gapped network. Models can be frontier, open or private, and are swappable per step.