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.
Most AI failures aren’t about the model. They’re about the missing layer between the demo and the system that has to operate.
- Demo to systemthe missing 80%
- Eval gatesnon-negotiable
- Cost SLOtracked weekly
Where we deploy AI · §02
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 · §03
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 }Quality, latency, cost · §04
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 79 warn 3 fail 2
Pass rate
94.0%
Suite
84 tests
Cadence
every release
Make the trade-off visible · §05
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.
Plan the economics · §06
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 30–50% savings vs the first week's bill, with no quality regression.
AI maturity · §07
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
Reference architecture · §08
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
Capability surface · §09
Different problems need different AI shapes.
A RAG copilot is not the right tool for an autonomous agent, and the other way around. Our delivery starts with picking the right shape for the job.
Reasoning
Generative
Tool-use
Vision / OCR
Document AI
Real-time
Fine-tuning
Vector / graph
Delivery path · §10
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 · §11
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
Governance & safety · §12
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
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
+91 79808 07674
Operations hours
Mon to Sat · 09:00 to 19:00 IST
Project teams cover follow-the-sun.