Context & Memory Management
Context engineering and RAG pipeline development decide what the model sees on every call: instructions, knowledge retrieved from enterprise systems, conversation history and memory, filtered by who is asking and sized to fit the window.
What is this layer about
A model only knows what is placed in front of it. Every answer is shaped by the prompt, the documents retrieved, the history carried forward and the memory recalled. If that assembly is careless, even the best model produces answers that are stale, incomplete or exposed to the wrong reader.
Enterprise data makes this harder. Documents carry permissions, records carry classifications, and conversations carry personal details. The context layer retrieves only what the requesting user is entitled to see, labels it by sensitivity, and keeps restricted content out of the window before the model ever reads it.
Windows are finite and tokens cost money. This layer compacts long histories, ranks and trims retrieved passages, and enforces a budget per request. The result is a prompt that is relevant, permitted and affordable, assembled the same way each time and open to inspection afterwards.
What do we deliver
Prompt assembly
Build each prompt from versioned templates, system instructions, user input and retrieved material in a defined order, so the same request is composed the same way every time.
Access-aware retrieval
Retrieve over documents, databases and knowledge bases with the caller's permissions applied at query time, so a user never receives content they could not open directly.
Index management
Maintain vector and keyword indexes over enterprise sources, with chunking, metadata and refresh schedules, so retrieval reflects current content and can trace each passage to its source.
Conversation history and memory
Keep session history and longer-term memory per user or case, with retention limits, scoping and deletion, so continuity does not turn into uncontrolled data accumulation.
Context compaction
Summarize, deduplicate and trim long histories and retrieved passages, preserving the facts that matter, so tasks stay within the window without losing the thread.
Token budgeting and classification
Allocate a token budget per request and check the classification of every item entering context, so sensitive data is masked or withheld and size stays predictable.
Technology stack
- Vector and search stores
- pgvector
- Elasticsearch
- OpenSearch
- Azure AI Search
- Qdrant
- Milvus
- Embedding models
- OpenAI text-embedding-3
- Cohere Embed
- BGE
- E5
- Sentence Transformers
- Retrieval and orchestration
- LangChain
- LlamaIndex
- Haystack
- Semantic Kernel
- Memory and session storage
- Redis
- PostgreSQL
- MongoDB
- Amazon DynamoDB
- Tokenization and data classification
- tiktoken
- Hugging Face Tokenizers
- Microsoft Presidio
- Apache Tika
These are the kinds of technologies this layer is typically built from; the choice stays with the customer.
Business outcome
Answers grounded in current company data. Responses draw on the right documents and records, with sources attached, so teams can check an answer against the material it came from.
Sensitive data stays out of the prompt. Permissions and classification are applied before context is built, which reduces the chance that restricted or personal data reaches a model or another user.
Predictable spend per request. Budgets and compaction keep prompt size within set limits, so the cost of a workload can be planned instead of discovered on the invoice.
Continuity without unchecked retention. Users and agents keep the thread of long work while retention, scope and deletion rules decide what is remembered and for how long.
Frequently asked questions
What does the context layer actually control?
It controls what the model sees on each call: instructions, retrieved company data, conversation history and memory. It decides what goes in, in what order, and how much fits the window and the budget.
How is sensitive data kept out of the prompt?
Retrieved items are checked against the caller's permissions and their classification before the prompt is built. Restricted content is masked or withheld, so it never reaches the model in the first place.
Who can see what the harness remembers?
Memory is scoped per user or case, with retention limits and deletion rules. A user's retrieval is limited to material they could open directly, and what was assembled can be inspected afterwards.
How does this layer relate to the layers around it?
It feeds the Model and Inference layer a prepared prompt and follows the rules set in Safety and Policy Controls. Agents in the runtime layer call on it for retrieval and memory during multi-step work.
Questions about your AI rollout?
Talk to us about how the seven layers fit your models and data.