8 wks
to first ROI signal
AI Strategy
Find the few use-cases worth building. Size them, sequence them, and pick the right architecture before a single GPU is spun up.
AI Lab · Live capabilities
AI that survivesproduction.
Most AI projects fail at the integration, not the model. We bring the missing layer: eval harnesses, guardrails, observability, and senior product engineering, so the AI you ship is the AI you can actually operate.
Eval-gated releasesVendor-neutralSOC 2 / ISO 27001 aligned
telematrix-ai · production · /observability
liveP95 latency
1.18s
↘ −12% vs 24h
Tokens / 24h
1.25M
rolling
Cost / req
$0.0143
↘ −8% vs 7d
Eval pass
97.4%
golden set
Tokens / minute+186 / min
$
ROUTEclaude-4.5-sonnet · 612t · 0.84s · pass
ok97.4%
Eval pass on golden sets
1.2s
P95 latency at the edge
−42%
Avg model spend after wk 4
0
Policy incidents in 90 days
What we build · §01
Six AI pillars, designed to compose.
Margin note
Most engagements use two or three of these pillars together. The interesting work is at the seams.
100%
answers cited
Generative AI / RAG
Domain-tuned copilots, retrieval-augmented systems, and customer-facing assistants that don't make things up.
0
policy incidents · 90d
AI Agents
Autonomous and human-in-the-loop agents with tool-use, memory, and the guardrails ops actually trust.
−28%
downtime
Predictive ML
Forecasting, propensity, anomaly detection wired into the systems that act on the prediction.
99.4%
page extraction
Vision & Multimodal
OCR, document AI, image and video understanding for ops, healthcare, and industrial use cases.
1.4T
tokens indexed
Data foundations
The unglamorous infra that makes AI feasible: warehouses, vector stores, lineage, and PII redaction.
See it run · §02
Pick a prompt. Hit run. Watch the engine work.
Token-by-token streaming with eval gates firing live, citations populating as they’re retrieved, and cost ticking up to the fourth decimal. The trace below is what an actual production agent looks like, not a stylised demo.
Try this
Pick any of the four prompts on the left, hit Run, and watch every phase, every gate, and every citation appear in real time. Stop or reset whenever.
live · prompt → answer · model in the loop
Try it on something real.
№ 04 · /playgroundready
§ Pick a prompt
claude-4.5-sonnet
§ Eval wall
- eval :: citation_requiredpending
- eval :: numeric_groundedpending
- guard :: pii_scrubpending
Gates run inline. Failures hard-block the response.
claude-4.5-sonnetctx: 200k · output cap: 8k · temperature 0.2
tok · 0/101t · 0ms$ · $0.00000
$ awaiting prompt · press Run to stream the response for Summarise Q3 revenue with citations.
Footnotes · grounded sources
0 / 3- 1finance/q3_review.pdfp.4
- 2warehouse://fact_revenuerows 1..12
- 3finance/q3_review.pdfp.11
§ Trace · agent decisions, in order0 / 5 phases
plan
retrieve
reason
guard
respond
Responses are pre-recorded representative outputs for the use cases we ship. Production traces look identical.rev · 2026.06 · session tmx-0001b8
Where we deploy AI · §03
Pick a use case. See the recipe we’d ship.
Each entry below is a real engagement pattern we run, with the model recipe, eval focus, time-to-pilot, and the architecture sketch we’d build first.
How to read this
Click a use case on the left. The right panel shows the architecture, models, and the why.
Pick a use case
See the recipe.
Operations
Customer support agent
−71%
average handle time
Shape
RAG agent · ticket-aware tools
Models
Claude Sonnet (reason) + Haiku (triage)
Time to pilot
6 to 8 weeks
Eval focus
- Refusal accuracy
- Citation required
- Tone match
Architecture sketch
data flow → left to rightTriage
Knowledge
Reason
Refusal
CRM tools
Reply
Why we build it this way
Most contact-centre teams burn 40% of agent capacity on tier-1 questions a copilot can answer with citations. We start there, expand from there.
How a run actually looks · §04
Watch one agent run, frame by frame.
Real production agents are a sequence of small decisions, tool calls, and verifier checks. Hit play, or click any step to jump to that frame.
8
steps in the run
2.0s
end-to-end latency
2
eval gates · pass
Trace replay · prod://customer-9341 · 2026-04-30T11:42Z
One agent run, frame by frame.
healthy
elapsed
0.22s
of 1.95s total
tokens
0
across all steps
cost
$0.0000
agent run
Timeline0s · 1.95s
0.00s0.49s0.97s1.46s1.95s
planstep 1 / 8
Plan
duration · 220mstag · claude-4.5-sonnet
input
show me last quarter's revenue by region, with the YoY change for each
output
needs: { warehouse.query, search.docs(footnotes), tabular_response }Trust is structural · §05
Every claim, tied to a source.
Hover any citation pill to see the source chunk it came from. Hover any source chunk to see which claims it supports. Citation-required is a hard production gate; answers that cannot cite get sent back to retrieval or refused outright.
Why this matters
The first question a regulator or board member asks is “where did that number come from?” If the answer cannot point at a source, the AI never ships.
№ 04 · answers, with receipts.
Every claim, tied to a source.
answer:9341-c · claude-4.5-sonnetlive · retrieval refreshes every 5m
user
cfo@telematrix-internal
question
"How did Q3 land regionally, and what should I worry about going into Q4?"
retrieved
3 docs · 8 chunks · 142ms
§01 · sources retrieved
k=3 · top of 8Q3 board memo · Finance.pdf
Finance · PDF · 14 pages · p.4-5
score · 0.91
retrieved 11:42:08Z
Operating discipline across the quarter held the gross margin within the band the audit committee approved in July. A1Q3 closed at $48.2M in NAM revenue, +12.4% YoY, with services attach climbing to 31% of the mix. Forward guidance for Q4 was reaffirmed in the same memo, contingent on the EMEA hiring plan landing on schedule. A2Services attach climbed to 31% of the mix, up from 24% in the prior comparable period.
warehouse://fact_revenue
Snowflake · materialised view · rows 1-12
score · 0.97
retrieved 11:42:09Z
Pulled fresh against the production warehouse, partition pruned to fiscal_quarter = 2026Q3. B1EMEA $31.4M (+6.8% YoY) · APAC $22.1M (+18.9% YoY) · LATAM $4.7M (+2.1% YoY). Row-level lineage verified against the upstream Salesforce export at 11:31Z (cache miss; query executed live).
Risk register · 2026-Q3.docx
Risk · Word doc · §7 · p.11
score · 0.88
retrieved 11:42:10Z
The risk register flags two items currently open against the regional plan; both are tracked weekly by the regional GMs. C1APAC growth carries a single-customer concentration risk: one logo accounts for 41% of in-quarter bookings. Mitigation is in progress: a named-account plan was approved on 2026-09-14 with a 90-day diversification target.
§02 · composed answer
412 tokens · 0.48s TTFTIn Q3, NAM revenue closed at $48.2M, up 12.4% YoY. EMEA finished at $31.4M (+6.8%) and APAC at $22.1M (+18.9%), with LATAM contributing $4.7M. Services attach is now 31% of mix, up from 24% in the prior comparable period. The single risk worth flagging into Q4: APAC growth is concentrated in one logo that accounts for 41% of in-quarter bookings, with a 90-day diversification plan already approved.
cited claims
4 / 4
every claim has a source
unsupported
0
eval::citation_required · pass
freshness
< 24h
all sources within budget
Citation-required is a hard gate in production. Answers that can’t cite are sent back to the retrieval step or refused outright.
eval::citation_required · v3.2 · last evaluated 11:42:11ZOur taxonomy · §06
A periodic table of AI capabilities.
Twenty-four capabilities across six families. Most production systems we ship combine seven to ten. Click any element for the way we actually deploy it.
On the table
The taxonomy gets revised quarterly. New elements move from explored to piloted to in production as our engagements graduate them.
the elements · §11
Our periodic table of AI capabilities.
margin note
Twenty-four elements, six families. Most products combine seven to ten.
ReasoningRetrievalToolsSafetyMultimodalEconomicsclick any element
010203040506070809
Rsn
Ret
Tls
Sfy
Mml
Ecn
Select any element to read its description, where we use it, and how mature it is in our stack.
24 elements · 6 families · last revised 2026-05-18
Reference architecture · §07
Planner. Tools. Memory. Guardrails.
We build agents the way we build distributed systems: with contracts, traces, and a bias for the boring choice. The result is a system that gets cheaper and better every week.
Guardrails first: refusal logic, policy checks, PII scrubbing
Tool-use orchestration with retries, fallbacks, and cost limits
Eval harness gates every release including prompt edits
Reference architecture
Seven layers, one accountable team.
Surface
Where humans and systems meet the AI · APIs, copilots, agents, embedded UIs.
Web SDKStreaming APIChat UISlack / Teams
Orchestration
Planner, tools, memory, retries · the operating system of the agent.
LangGraphTool routerCost limitsRetries
Guardrails
Safety, refusal, policy as code · the layer that keeps AI honest.
Refusal logicPII scrubPolicy DSLRed team
Models
Vendor-neutral routing across closed and open weights, picked per job.
ClaudeGPTGeminiLlama / Mistral
Knowledge
Retrieval, vectors, lineage · the data layer the AI is allowed to see.
pgvectorPineconeQdrantLineage
Observability
Token, latency, cost, eval per agent and per prompt, every release.
Eval harnessTracesDashboardsCost SLO
Foundation
VPC, KMS, IAM, audit · the boring infrastructure your security team likes.
AWS / GCP / AzureVPCCMKAudit logs
Quality, latency, cost · §08
We pick the model that wins for your job.
No religious affiliation with any vendor. The leaderboard rebalances weekly on your real workload, and every release walks across the eval grid before it ships.
Model benchmark · live router
Pick the model that wins for your job.
Claude 4.5 Sonnet
Anthropic
96
GPT-4o
OpenAI
94
Gemini 2.5 Pro
92
Claude 4.5 Haiku
Anthropic
88
Llama 4 70B
Open weights
86
Mistral Large 2
Mistral
84
Sorted by quality (eval pass on golden set). Numbers are typical of routes we run in production · vendor-neutral · refreshed weekly.
Eval harness · golden set
Quality is measured every release.
pass 77 warn 7 fail 0
Pass rate
91.7%
Suite
84 tests
Cadence
every release
Make the trade-off visible · §09
Move the weights. Watch the winner change.
Quality, latency, and cost almost never agree. Slide the three weights to your job’s real shape and the router recomputes the recommended model in real time.
Margin note
In production, the router rebalances weekly with real traffic. Most accounts shift away from frontier models by month two · quality holds, cost drops 30 to 50%.
Router playground
Move the weights. Watch the winner change.
Quality50%
Latency30%
Cost20%
Router pickscore 0.890
Claude 4.5 Haiku
Anthropic · fast triage / classification
Quality
86
weight · 50%
Latency
92
weight · 30%
Cost
92
weight · 20%
Claude 4.5 Haiku wins because the eval scores hold under quality-first weighting. We'd still send fast lanes (greetings, retries) to GPT-4o-mini.
Contenders · sorted by weighted score
- 01
Claude 4.5 Haiku
Anthropic · fast triage / classification
0.89 - 02
GPT-4o-mini
OpenAI · cheap multi-step
0.86 - 03
Mistral Large 2
Mistral · EU / data residency
0.80 - 04
GPT-4o
OpenAI · general workhorse
0.79 - 05
Gemini 2.5 Pro
Google · multimodal & long context
0.78 - 06
Llama 4 70B
Open weights · self-host / on-prem
0.78 - 07
Claude 4.5 Sonnet
Anthropic · frontier reasoning
0.78
Live router weights · vendor-neutral · rebalanced weekly in production.
The tokens go somewhere · §10
Where every token spends its life.
Most teams budget tokens as one number. The real picture is five buckets. Move the sliders to see how your context strategy changes the bill; the ratio is the lever, the model is mostly downstream.
the tokens go somewhere · §10
Where every token spends its life.
single-request cost model
№ 01 · allocationdrag to reallocate
- № 01System promptinput
Persona, policy, tool descriptions, format contract. · max 2,000 tok
320tok
$0.00096
- № 02Context (RAG)input
Retrieved chunks, doc snippets, memory hits. · max 8,000 tok
1,200tok
$0.00360
- № 03Tool resultsinput
JSON payloads streamed back from function calls. · max 4,000 tok
180tok
$0.00054
- № 04User inputinput
The literal turn the user typed (or transcribed). · max 1,500 tok
90tok
$0.00027
- № 05Response (out)output
What the model actually generates back. Always priced higher. · max 4,000 tok
480tok
$0.00720
№ 03 · distribution
hover for detail
- System prompt14.1%
- Context (RAG)52.9%
- Tool results7.9%
- User input4.0%
- Response (out)21.1%
Total tokens / request
2,270
2270 tokens across 5 buckets
Cost / request
$0.0126
Claude 4.5 Sonnet · in + out
Cost / 1M requests
$12,570
extrapolated · linear
If you 10× volume, the cheapest savings hide in the context bucket. Trimming retrieval by 30% almost always beats swapping the model.
Plan the economics · §11
Estimate the bill before the first request fires.
Tweak the dials and see how request volume, token shape, and model tier move the monthly spend and the latency budget.
Cost & latency calculator
Plan AI economics before you ship.
50k
900 t
Model tier
Estimated monthly spend
$137
~ $0.0027 / request · indicative, exclusive of infra
P95 latency
0.54s
Total tokens
45.0M
We tune the router weekly. Most accounts see 30 to 50% savings vs the first week's bill, with no quality regression.
essay · §15field journal · vol. iv
Most AI projects do not die because the model was wrong. They die at the integration. They die in the quiet weeks after the demo, when somebody has to wire the thing to a payments table, a retry budget, a legal review, and a Tuesday morning incident channel. The model already worked. The team around the model did not yet exist.
§
The shocking thing about working on production AI, once you have done it for a year, is how boring the hard parts are. Golden eval sets. Observability. Retry budgets. Rollback paths. Versioned prompts. A small but stubborn list of seventeen prompts that regressed when you switched from Claude 4.5 Sonnet to Gemini 2.5 Pro and have to be tagged so they never auto-route again. None of this is on a leaderboard. All of it is what separates a working system from a clever notebook.
The interesting question in 2026 is not which model is smartest. That question has been answered, repeatedly, in both directions, by every frontier lab in turn, and the answer keeps changing every eleven weeks. The interesting question is which team can ship the system around the model. The system that survives Monday morning. The system that survives a regulator. The system that a new engineer can read on her first day and not be afraid of.
A recent client moved from a single-vendor frontier setup to a vendor-neutral router. The router dropped quality by 0.3% on their golden evals. Cost dropped 47%. They did not ship the router because of cost. They shipped it because their PM could finally promise an SLA to a client without losing sleep on Sunday night. That is the trade we are actually in.
meta · the argument in one paragraph
Quality is not the thing you measure once at launch. Quality is the thing you keep measuring in production, on data your customers actually send you, while the world quietly shifts underneath. A model that is six points smarter on a public benchmark is not a better product if you cannot tell, on Tuesday at 4 p.m., whether it just got worse for half of your French users. The teams who win are the ones who built the boring instrument before they bought the fast car.
So a small note on the rest of this lab. Every claim you see in the panels around this essay is something we would argue for under pressure from a procurement team. The cost figures are numbers we have actually paid. The latencies are p95s we have actually shipped. The trade-offs are trade-offs we have lost sleep over. None of it is marketing. We would rather be honest than impressive, because the only AI work worth doing is the kind that holds up on a Monday[1].
end · field journal, entry 15
[1] Our golden evals are run on customer-specific data, never on public benchmarks. Public benchmarks are leaderboards; production is not. If a vendor cannot describe how their quality numbers were generated, treat the numbers as aspirational rather than operational.
[note] Client and project details in this essay are composites. The numbers, the trade, and the Sunday night are not.
filed under · integration, evaluation, routingtelematrix · ai lab · the field journalset in display sans, 2026
AI maturity · §13
Five stages from curious to differentiating.
Click a stage to see what it looks like in practice and what the next move usually is. Most teams we work with sit between Piloting and Operating.
AI Maturity model
Where is your team today?
Stage 3 · Senior delivery
Operating
Multiple AI surfaces in production with real eval coverage, on-call, and weekly cost review. Engineering treats AI like any other system.
The next move
Standardise on a vendor-neutral router, push more workloads to private cloud where it pays off, expand eval to behavioural tests.
What this looks like in practice
- Versioned prompts and models
- Eval gates on releases (golden + redteam)
- Per-agent cost telemetry
- PII redaction and policy as code
incidents · §17
Six incidents we caught. Zero escaped.
register · trailing 90d
Register · trailing 90 days
Filed in reverse chronological order.
6 entries · ordered desc
- 2026-02-14·14:08 UTC·№ 01P2support-triage · v4.2.1INC-2026-0214
Support-triage agent looped on a stale Zendesk token, burning 3 days of budget.
After a rotated API key, the agent kept retrying the same failing tool call on every conversation refresh. By the third day the retry tax was visible on the cost dashboard, not on any alert.
caught by
guard::tool_retry_budgetpass · 14:08:42what changed · permanent
Hop limit set to 6 with exponential backoff. Added a retry-spike behavioural eval to the golden set, and a token-burn-rate alert at 1.4x baseline.
- 2026-02-03·09:21 UTC·№ 02P2policy-explainer · v1.8.0INC-2026-0203
RAG answer invented a footnote citing a doc that did not exist in the index.
The model fabricated a plausible-looking citation, 'policies/refunds_v3.pdf', for an unsupported numeric claim. Retrieval had returned only adjacent chunks, none containing the figure.
caught by
eval::citation_requiredpass · 09:21:11what changed · permanent
Every numeric claim now hard-fails the response unless it cites an actual retrieved chunk by hash. Two regression cases added to golden set 0x07.
- 2026-01-19·22:47 UTC·№ 03P1billing-replies · v2.0.3INC-2026-0119
Outbound draft contained a customer's full DOB in the salutation line.
A template merged a CRM field meant for verification into the visible greeting. Redaction caught the date pattern before the message left the queue; no email sent.
caught by
guard::pii_redactionpass · 22:47:03what changed · permanent
PII redaction promoted from advisory to blocking on outbound surfaces. New eval: 240 synthetic DOB / SSN / IBAN injections; current pass-rate 100%.
- 2025-12-07·03:14 UTC·№ 04P2research-assistant · v3.1.0INC-2025-1207
Indirect prompt injection inside a scraped PDF tried to exfiltrate the system message.
A weekly red-team replay surfaced the attack pattern against a staging build. The injected page asked the agent to repeat its instructions verbatim and email them to an attacker-controlled URL.
caught by
redteam::injection_corpuspass · 03:14:55what changed · permanent
Tool calls to outbound HTTP now require a domain allowlist. The injection corpus expanded to 1,420 cases; gated as a release blocker, not a warning.
- 2025-11-22·11:02 UTC·№ 05P3router::default · v0.9.7INC-2025-1122
Cost-per-request silently doubled after a routing change pushed long-context to a premium model.
A model swap from Claude 4.5 Sonnet to a larger frontier model fired for any request over 24k tokens. Per-request cost moved from $0.0086 to $0.0181 over a single afternoon without an alert page.
caught by
alert::token_budgetpass · 11:02:18what changed · permanent
Token budget alert added at 1.2x rolling p50. Routing rules now require a cost-impact review in the same PR. Default escalates to cheaper distillation, not premium.
- 2025-10-09·16:35 UTC·№ 06P3compliance-q&a · v1.4.2INC-2025-1009
Compliance assistant refused a legitimate disclosure question, citing a policy that did not apply.
An over-cautious system prompt caused the agent to refuse a routine SOC 2 question from an authenticated auditor. The user retried twice, escalated, and a human had to answer.
caught by
eval::behavioural_refusalpass · 16:35:40what changed · permanent
Behavioural eval added: 86 over-refusal cases sourced from real escalations. System prompt rewritten to allow disclosure questions from authenticated audit roles, with a smaller policy footnote.
end of register · cursor 90d
Governance & safety · §15
How we keep AI honest.
Every system we ship has the receipts your security and risk teams will ask for. No black boxes. No hand-waving.
Eval harness gates every release including prompt edits
PII redaction, isolated tenancy, customer-managed keys
Citation-required answers, refusal logic, policy-as-code
Token, latency, and quality dashboards per agent and prompt
Versioned prompts and models with rollback in seconds
Continuous eval against golden sets and red-team probes
Delivery path · §16
Six to ten weeks to a real production pilot.
No 12-week pilots that never ship. We start by writing the test set, build with guardrails, run a controlled rollout, then operate.
Week 1 to 2
Discover & evaluate
Use-case scoring, eval set built from your data, success metrics tied to a sponsor.
Week 3 to 6
Build with guardrails
RAG, tools, memory wired in. Refusal logic, PII scrubbing, citation requirements live from day one.
Week 7 to 10
Pilot in production
Controlled rollout with eval gates, weekly cost & quality review, on-call coverage.
Week 11 onward
Operate & compound
Vendor-neutral routing tuned weekly, behavioural eval expanding, cost down, quality up.
Deployment · §17
Cloud, private cloud, or on-prem. Your call.
Managed Cloud
Vendor-managed inference. Fastest time to value. Works for most use-cases.
- OpenAI · Anthropic · Vertex
- Cost & rate-limit management
- SOC 2-aligned by default
Private Cloud / VPC
Models run inside your AWS, GCP, or Azure. No data leaves your perimeter.
- Bedrock · Vertex · Azure AI
- Customer-managed keys
- VPC-only egress
On-prem / Air-gapped
For regulated and offline environments. Open-weights or licensed models on your hardware.
- Llama / Mistral / Qwen
- vLLM · Triton · TGI
- Audit-ready logging
Toolbelt
We use the right tool for the job.
OpenAIAnthropicGoogle GeminiMeta LlamaMistralHugging FaceLangChainLangGraphLlamaIndexDSPyPineconeWeaviatepgvectorQdrantModalVercel AI SDKTemporalTriton
AI in production
Engagement patterns we’ve shipped.
Principles
How we build AI that holds up under scrutiny.
01
Eval-first delivery
We build the test set before we build the system. Quality is measured every release, not estimated.
02
Vendor-neutral
Closed-weight, open-weight, on-prem, hybrid. We pick the model that wins on quality, latency, and cost.
03
Privacy by construction
PII redaction, isolated tenants, no training on customer data unless explicitly contracted.
04
Cost as a feature
Token, GPU, and storage cost is tracked at the agent and prompt level every week.
AI Lab · FAQ
The questions sponsors actually ask.
Don’t see your question? Drop us a line and you’ll hear back from a senior engineer, not a sales rep.
How fast can we get something into production?
First production-grade pilot in 6 to 10 weeks. The first two weeks are evaluation harness and use-case scoping, the next four are build with guardrails, then a controlled production rollout. We do not believe in 12-week 'pilots' that never ship.
Are you locked into a particular model vendor?
No. We are vendor-neutral and route per job to whatever wins on quality, latency, and cost. Most production systems we operate use a mix of Claude, GPT, Gemini, and at least one open-weights model behind a router we manage on your behalf.
Where does our data live? Can we run on-prem?
We deploy in three flavours: managed cloud, private cloud / VPC, and fully on-prem or air-gapped. For regulated workloads we standardise on open-weights models with VPC-only egress, customer-managed keys, and audit-ready logging.
How do you measure quality?
We build the eval set before we build the system, on your real data. Every release runs against a golden set plus behavioural and red-team probes, and quality regression blocks the release. You get the eval scores in a weekly executive report.
What happens if a model is deprecated mid-engagement?
Models change underneath us all the time. Because routing is vendor-neutral and protected by an eval gate, we can swap models in a release without a regression in your product, often with a cost reduction.
Engage a specific AI capability
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Direct line
support@telematrixglobal.com
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