Agents
Agents and agentic workflows
Agents that work with your enterprise systems through governed access, with approval steps where a person must decide.
AI transformation · Applied AI
Applied AI Engineering
Silex takes AI use cases from proof of concept to supported services. We connect agents to your systems through governed access, build retrieval on your data, and evaluate every model and agent before it reaches production.
Techniques we use
- LoRA and QLoRA fine-tuning
- Synthetic data pipelines
- Constrained decoding
- Evaluation harnesses
- Retrieval-augmented generation
- Red-teaming
How it works
Pilot to production, one gate at a time.
Each stage ends with a gate that you and Silex agree on before work starts. A use case moves to the next stage only when it passes.
01Prototype
A working proof of concept on representative data, built with your team, with the success criteria written down before work starts.
Exit gate
Success criteria and a test set agreed with you.
02Evaluate
An evaluation harness tests the model or agent against realistic cases with graded criteria and records the results for every version.
Exit gate
The harness meets the pass rate agreed with you.
03Harden
The system reaches data and tools only through the AI and MCP gateway, and model output is validated against schemas before other systems use it.
Exit gate
Security testing and red-teaming are complete, and you have fixed or accepted each finding.
04Serve
The model runs on sized infrastructure, in your environment or through a hosted service, with capacity, latency, and cost monitored.
Exit gate
Serving, monitoring, and runbooks are in place, with governed access through the gateway.
05Operate
Forward Deployed Engineers run the service with your team, onboard new use cases, and transfer ownership on your schedule.
Continuous check
Every model, prompt, or data change reruns the evaluation before release.
What we deliver
AI systems your team can support.
Silex builds the parts of an AI system that decide whether it can run in production: governed access, data, evaluation, security, and serving.
Retrieval
Retrieval-augmented generation
Retrieval on your documents and data, with access controls carried from the source systems into the index.
Evaluation
Evaluation harnesses
Tests that measure models and agents against realistic cases before production and after every change.
Training
Fine-tuning and synthetic data
Tuned open-weight models and synthetic training data, used where the use case requires them and prompting alone does not meet the criteria.
Security
Security testing and red-teaming
Tests for prompt injection, data leakage, and unsafe tool use, with findings fixed before release.
Private AI
Private AI deployments
Models served in your environment, with model serving and inference optimization sized to the workload.
Experience
From our own model development.
Silex engineers have built, trained, and evaluated their own models. That work is the basis for the methods we bring to your use cases.
- Fine-tuning
Silex has fine-tuned open-weight models, including Mistral, Gemma, and Llama, for classification and generation with LoRA and QLoRA, running distributed training with FSDP and DeepSpeed on Amazon SageMaker.
- Synthetic data
Silex has built multi-stage synthetic data pipelines that plan, generate, re-score with a second model, and label against a closed vocabulary, for domains where real data cannot be used for training.
- Structured output
Silex has constrained model output with JSON schemas, Pydantic models, and constrained decoding, so the systems that consume the output receive valid data.
- Rules and models
Silex has combined deterministic domain rules with model predictions, for example by encoding medical coding logic in code alongside the model.
- Multi-model serving
Silex has served multi-model pipelines, with classifiers feeding a generator, on NVIDIA Triton and vLLM on GPU Kubernetes, behind a service mesh with mutual TLS and signed-image CI/CD.
- Real-time feedback
Silex has built interfaces that return model feedback in real time as a user types.
- Serving benchmarks
Silex has benchmarked attention implementations, quantization, and vLLM throughput to choose serving configurations.
Choosing the approach
Start with the simplest approach that passes.
Most AI systems combine these approaches. Silex tests each option against your evaluation set and recommends the least complex one that meets the criteria.
| Approach | When it fits | What you maintain |
|---|---|---|
| Prompt and context engineering | The model already has the knowledge and skills the task needs, and the work is clear instructions, examples, and output schemas. | System prompts, examples, and schemas, versioned and tested. |
| Retrieval-augmented generation | Answers depend on your documents or data, the content changes over time, or responses must cite their sources. | The ingestion pipeline, the index, and access controls on the source data. |
| Fine-tuning | The task needs a consistent format, classification behavior, or domain vocabulary that prompting does not produce, or a smaller model must handle a narrow task. | Training data, tuned model versions, and a retraining process. |
| Synthetic data | Real data is scarce, sensitive, or cannot be used for training or evaluation. | The generation pipeline, the scoring model, and expert review of samples. |
Approach
Prompt and context engineering
When it fits
The model already has the knowledge and skills the task needs, and the work is clear instructions, examples, and output schemas.
What you maintain
System prompts, examples, and schemas, versioned and tested.
Approach
Retrieval-augmented generation
When it fits
Answers depend on your documents or data, the content changes over time, or responses must cite their sources.
What you maintain
The ingestion pipeline, the index, and access controls on the source data.
Approach
Fine-tuning
When it fits
The task needs a consistent format, classification behavior, or domain vocabulary that prompting does not produce, or a smaller model must handle a narrow task.
What you maintain
Training data, tuned model versions, and a retraining process.
Approach
Synthetic data
When it fits
Real data is scarce, sensitive, or cannot be used for training or evaluation.
What you maintain
The generation pipeline, the scoring model, and expert review of samples.
Platforms
What we build with.
Select Tier Services Partner
Certified Delivery Partner
Premier Partner
Anthropic Claude · Amazon Bedrock · Amazon SageMaker · Azure OpenAI · Open-weight models such as Llama, Mistral, and Gemma · vLLM · Kong AI Gateway
Questions buyers ask
Questions
Should we fine-tune a model or use retrieval?
Use retrieval when answers depend on your documents or data, especially content that changes or must be cited. Fine-tune when you need a consistent format, classification behavior, or domain vocabulary that prompting does not produce. Many systems use both, and we test each option against your evaluation set before we recommend one.
How does Silex evaluate models and agents?
We build an evaluation harness from realistic cases that your experts help define, with graded criteria for each case. Every model, prompt, or configuration change runs through the harness, and the results are recorded so you can compare versions.
Can the system run privately in our environment?
Yes. Silex can deploy open-weight models on your infrastructure with vLLM, or use hosted models in your cloud account through Amazon Bedrock or Azure OpenAI. Self-hosted and hosted models can share one set of controls through the AI gateway.
What if we do not have training data?
Synthetic data can fill the gap. Silex builds pipelines that plan cases, generate examples, re-score them with a second model, and label them against a fixed vocabulary. Your experts review samples before the data is used.
How are AI systems security tested?
We test prompts, tools, and outputs against the OWASP Top 10 for LLM Applications, including prompt injection and data leakage, and we red-team agents that call tools. You fix or accept each finding before the system moves to production, and gateway guardrails stay in place afterward.
Next step
Book a Pilot-to-Production Workshop.
We work with your team on a plan to move one AI use case from proof of concept to a supported service.