Memory, engineered end to end.
Five Service Lines that cover how enterprise AI stores, retrieves, reconciles, secures, and measures what it knows. Most clients start with an Assessment and continue into an Engagement across the lines it shows they need.
Context Architecture
The model sees the right facts, in the right shape, within budget.
We design the structured context store behind your assistants and agents: what gets remembered, in which schema, for how long, and how it is assembled into the context window at inference time.
What we build
- Typed memory schemas for entities, facts, events, and preferences, in place of raw transcript dumps
- Bi-temporal records that keep when a fact was true separate from when the system learned it
- Context assembly pipelines with explicit token budgets and priority tiers
- Lifecycle policies for consolidation, summarization, decay, and deletion
Problems it fixes
- Agents that ask again for information they were already given
- Prompts padded with history that has nothing to do with the task
- Context windows that silently truncate the facts that matter
Retrieval Engineering
The facts that answer the question are the ones that get retrieved.
Vector search alone misses exact identifiers. Keyword search alone misses paraphrase. We build hybrid retrieval tuned to your documents, your queries, and your latency budget.
What we build
- Hybrid lexical (BM25) and dense retrieval with score fusion
- Cross-encoder reranking and metadata filtering
- Graph traversal for entity-linked memory
- Query rewriting and routing across multiple stores
- Chunking and indexing strategies matched to how your documents are written
Problems it fixes
- Part numbers, contract IDs, and names that never match
- Near-duplicate passages crowding out the one that answers
- Latency and cost driven up by retrieving far more than the model uses
Memory Integrity
When facts change or disagree, the system knows which one to trust.
Enterprise memory is written by many sources over months and years. We make it consistent, attributable, and deletable, so a correction made once stays corrected.
What we build
- Memory Conflict detection across time, across sources, and between inferences and documented facts
- Resolution policies based on recency, source authority, and provenance, with escalation to a person when confidence is low
- Provenance on every record: source, author, timestamp, and how it was derived
- Right-to-erasure workflows that remove a person’s data from stores, indexes, and caches
Problems it fixes
- Stale facts that outlive their corrections
- Two systems of record that disagree without anyone noticing
- No way to answer “why did the assistant say that?”
Evaluation
You know whether a change made memory better or worse before your users do.
Memory systems degrade quietly. We build eval harnesses that measure retrieval and memory behavior directly and run on every change, from prompt edits to model upgrades.
What we build
- Golden datasets drawn from your real traffic, with sensitive data removed
- Retrieval metrics for recall, precision, and ranking quality, alongside end-to-end answer grading
- Long-horizon tests where facts are introduced, updated, and contradicted across sessions
- Regression gates in CI, with reports your team owns and extends
Problems it fixes
- Changes shipped because they looked better in a handful of chats
- Model upgrades that quietly break recall
- No baseline for comparing vendors, models, or architectures
AI Security
Untrusted content cannot steer your agents or reach memory it should not see.
Every document, email, and ticket an agent reads can carry instructions. Persistent memory means an injected instruction can persist too, and resurface weeks later.
What we build
- Prompt injection defenses: content isolation, instruction hierarchy, and output checks
- Memory poisoning detection, with quarantine before a write is committed
- Tenant and permission isolation enforced at retrieval time, not requested in the prompt
- Red-team suites for injection and data exfiltration, added to your eval harness
Problems it fixes
- Injected instructions stored as memories and replayed later
- Retrieval returning documents the user is not permitted to see
- Secrets and internal data echoed back in responses
The Context & Memory Assessment
About three weeks. Fixed scope. Priced after a scoping call.
A fixed-scope review of how your LLM applications remember, retrieve, and fail today, ending with a baseline you can measure against and a plan you can act on.
Week 1
Map
Architecture and data-flow review, interviews with the teams that build and run the system, and sampling of real traces.
Week 2
Measure
A baseline eval suite built on your traffic, plus targeted tests for staleness, Memory Conflicts, data leakage, and injection.
Week 3
Plan
A findings readout with your engineering and security leads, and a roadmap ordered by impact and effort.
Deliverables
- Current-state architecture map of your context and memory pipeline
- Failure-mode report: where memory goes stale, conflicts, leaks, or can be injected
- Baseline eval suite that your team keeps and runs
- Prioritized roadmap, with effort and expected impact for each item
We need read access to architecture documentation, a sample of production traces, and a few hours with the engineers who run the system. We can work inside your cloud and under your security review.
Request an Assessment